# -*- coding: utf-8 -*-
"""
Updated Oct 18 2022
@author: Qianliang Li (glia@dtu.dk)
This script contains the code to estimate the following EEG features:
1. Power Spectral Density
2. Frontal Theta/Beta Ratio
3. Asymmetry
4. Peak Alpha Frequency
5. 1/f Exponents
6. Microstates
7. Long-Range Temporal Correlation (DFA Exponent)
Source localization and functional connectivity
8. Imaginary part of Coherence
9. Weighted Phase Lag Index
10. (Orthogonalized) Power Envelope Correlations
11. Granger Causality
It should be run after Preprocessing.py
All features are saved in pandas DataFrame format for the machine learning
pipeline
Note that the code has not been changed to fit the demonstration dataset,
thus just running it might introduce some errors.
The code is provided to show how we performed the feature estimation
and if you are adapting the code, you should check if it fits your dataset
e.g. the questionnaire data, sensors and source parcellation etc
The script was written in Spyder. The outline panel can be used to navigate
the different parts easier (Default shortcut: Ctrl + Shift + O)
"""
# Set working directory
import numpy as np
import os
wkdir = "/home/s200431"
os.chdir(wkdir)
# Load all libraries from the Preamble
from Preamble import *
# %% Load preprocessed epochs and questionnaire data
load_path = "/home/s200431/PreprocessedData"
# Get filenames
files = []
for r, d, f in os.walk(load_path):
for file in f:
if ".fif" in file:
files.append(os.path.join(r, file))
files.sort()
# Subject IDs
Subject_id = [0] * len(files)
for i in range(len(files)):
temp = files[i].split("/")
temp = temp[-1].split(".")
temp = temp[0].split("_")
Subject_id[i] = int(temp[0])
# Should exclude subject 100326 due to 7 bad channels
# Exclude 200013 and 200015 due to too many dropped epochs
# (200001, 200004, 200053 and 200072 were already excluded prior to preprocessing)
# Exclude 302215, 302224, 302227, 302233, 302264, 302268, 302275 due to too many dropped epochs
# 13 subjects excluded in total + 4 I did not receive because they were marked as bad from Helse
bad_subjects = [100326, 200013, 200015, 302224, 302227, 302233, 302264, 302268, 302275]
good_subject_idx = [not i in bad_subjects for i in Subject_id]
Subject_id = list(np.array(Subject_id)[good_subject_idx])
files = list(np.array(files)[good_subject_idx])
n_subjects = len(Subject_id)
# Load ISAF
n_ISAF = 51-1
ISAF7_final_epochs = [0]*n_ISAF
for n in range(len(ISAF7_final_epochs)):
ISAF7_final_epochs[n] = mne.read_epochs(fname = os.path.join(files[n]),
verbose=0)
# Load HELSE
n_HELSE = 70-2
HELSE_final_epochs = [0]*n_HELSE
for n in range(len(HELSE_final_epochs)):
HELSE_final_epochs[n] = mne.read_epochs(fname = os.path.join(files[n+n_ISAF]),
verbose=0)
# Rename channels to match ISAF (Using 10-20 MCN)
mne.rename_channels(HELSE_final_epochs[n].info, {"T3":"T7",
"T4":"T8",
"T5":"P7",
"T6":"P8"})
# Warning about chronological order is due to interleaved EO->EC->EO->EC being concatenated as 5xEC->5xEO
# Load Baseline
n_Base = 91-6
Base_final_epochs = [0]*n_Base
for n in range(len(Base_final_epochs)):
Base_final_epochs [n] = mne.read_epochs(fname = os.path.join(files[n+n_ISAF+n_HELSE]),
verbose=0)
# I will use the union of the channels in both dataset (except mastoids)
# This means I add empty channels and interpolate when it is missing
# Helsefond montage has wrong calibration, head size are too big.
# Thus I will need to re-calibrate by comparing with ISAF7
# Notice I already used the final dig montage for Baseline data
# Get channel names
ISAF7_chs = ISAF7_final_epochs[0].copy().info["ch_names"]
Helse_chs = HELSE_final_epochs[0].copy().info["ch_names"]
Base_chs = Base_final_epochs[0].copy().info["ch_names"]
# Get intersection of channels
intersect_ch = list(set(Helse_chs) & set(ISAF7_chs))
intersect_ch_ratio = [0]*len(intersect_ch)
for i in range(len(intersect_ch)):
# Get channel name
ch_name0 = intersect_ch[i]
# Get index and electrode location
ISAF7_ch_idx = np.where(np.array(ISAF7_chs) == ch_name0)[0]
ISAF7_ch_loc = ISAF7_final_epochs[0].info["chs"][int(ISAF7_ch_idx)]["loc"]
Helse_ch_idx = np.where(np.array(Helse_chs) == ch_name0)[0]
Helse_ch_loc = HELSE_final_epochs[0].info["chs"][int(Helse_ch_idx)]["loc"]
# Calculate ratio
intersect_ch_ratio[i] = [ch_name0,ISAF7_ch_loc[0:3]/Helse_ch_loc[0:3]]
# Most of the ratios are either around 0.095 or 0/Inf when division with 0
# We also see that Cz for ISAF is defined as (0,0,0.095m). For Helse Cz is (0,0,1m)
Helse_to_ISAF7_cal = 0.095
# Now we are ready to make a combined montage (info["dig"])
# Get list of channels to add
ISAF7_add_ch_list = list(set(Helse_chs)-set(ISAF7_chs))
Helse_add_ch_list = list(set(ISAF7_chs)-set(Helse_chs))
combined_ch_list = Helse_chs + ISAF7_chs
# Remove duplicates while maintaining A-P order
duplicate = set()
final_ch_list = [x for x in combined_ch_list if not (x in duplicate or duplicate.add(x))]
# Move AFz and POz to correct position
final_ch_list.insert(3,final_ch_list.pop(-2)) # from second last to 3
final_ch_list.insert(-5,final_ch_list.pop(-1)) # from last to seventh from last
# The order of DigPoints in info are based on sorted ch names
Helse_dig = HELSE_final_epochs[0].copy().info["dig"]
Helse_chs_sorted = Helse_chs.copy()
Helse_chs_sorted.sort()
ISAF7_dig = ISAF7_final_epochs[0].copy().info["dig"]
ISAF7_chs_sorted = ISAF7_chs.copy()
ISAF7_chs_sorted.sort()
# Make one list with DigPoints from Helse + unique channels from ISAF7
ch_idx = [i for i, item in enumerate(ISAF7_chs_sorted) if item in set(Helse_add_ch_list)] # indices of unique channels
final_ch_list_sorted = final_ch_list.copy()
final_ch_list_sorted.sort()
dig_insert_idx = [i for i, item in enumerate(final_ch_list_sorted) if item in set(Helse_add_ch_list)] # find where ISAF7 channels should be inserted
# Prepare combined dig montage
final_dig = Helse_dig.copy()
# Calibrate Helse digpoints
for i in range(len(final_dig)):
final_dig[i]["r"] = final_dig[i]["r"]*Helse_to_ISAF7_cal
# Insert ISAF7 digpoints
for i in range(len(ch_idx)):
final_dig.insert(dig_insert_idx[i],ISAF7_dig[ch_idx[i]])
# Remove mastoids
Mastoid_ch = ["M1", "M2"]
M_idx = [i for i, item in enumerate(final_ch_list_sorted) if item in set(Mastoid_ch)] # find mastoids ch
M_idx2 = [i for i, item in enumerate(final_ch_list) if item in set(Mastoid_ch)] # find mastoids ch
M_idx3 = [i for i, item in enumerate(Helse_add_ch_list) if item in set(Mastoid_ch)] # find mastoids ch
for i in reversed(range(len(M_idx))):
del final_ch_list_sorted[M_idx[i]]
del final_dig[M_idx[i]]
# Mastoids are placed in the back of final_ch_list and Helse_add_ch_list and are also removed
del final_ch_list[M_idx2[i]]
del Helse_add_ch_list[M_idx3[i]]
# T7, T8, Pz, P8 and P7 are placed wrongly (probably due to renaming)
# This is fixed manually
final_dig.insert(-7,final_dig.pop(-4)) # swap between P8 and T7
final_dig.insert(-6,final_dig.pop(-3)) # swap between T7 and T8
final_dig.insert(-4,final_dig.pop(-4)) # swap between Pz and T7
final_dig.insert(-5,final_dig.pop(-5)) # swap between Pz and POz
# Update EEG identity number
for i in range(len(final_dig)):
final_dig[i]["ident"] = i+1
# Make final digital montage
final_digmon = mne.channels.DigMontage(dig=final_dig, ch_names=final_ch_list_sorted)
# final_digmon.plot() # visually inspect topographical positions
# final_digmon.plot(kind="3d") # visually inspect 3D positions
# final_digmon.save("final_digmon.fif") # Save digital montage
with open("final_digmon_ch_names.pkl", "wb") as filehandle:
# The data is stored as binary data stream
pickle.dump(final_digmon.ch_names, filehandle)
# Remove mastoids from ISAF, add channels from Helse and interpolate
for n in range(len(ISAF7_final_epochs)):
# Remove mastoid channels
ISAF7_final_epochs[n].drop_channels(Mastoid_ch)
# Add empty channels to interpolate - notice that the locations are set to 0
mne.add_reference_channels(ISAF7_final_epochs[n],ISAF7_add_ch_list,copy=False)
# Fix channel info (both after removal of mastoids and newly added chs)
# Ch info loc are linked for all reference channels and this link is removed
for c in range(ISAF7_final_epochs[n].info["nchan"]):
ISAF7_final_epochs[n].info["chs"][c]["loc"] = ISAF7_final_epochs[n].info["chs"][c]["loc"].copy()
ISAF7_final_epochs[n].info["chs"][c]["scanno"] = c+1
ISAF7_final_epochs[n].info["chs"][c]["logno"] = c+1
# Set new combined montage
ISAF7_final_epochs[n].set_montage(final_digmon)
# Set newly added channels as "bad" and interpolate
ISAF7_final_epochs[n].info["bads"] = ISAF7_add_ch_list
ISAF7_final_epochs[n].interpolate_bads(reset_bads=True)
# Fix "picks" in order to reorder channels
ISAF7_final_epochs[n].picks = np.array(range(ISAF7_final_epochs[n].info["nchan"]))
# Reorder channel
ISAF7_final_epochs[n].reorder_channels(final_ch_list)
# Add channels from ISAF to Helse and interpolate
for n in range(len(HELSE_final_epochs)):
# Add empty channels to interpolate
mne.add_reference_channels(HELSE_final_epochs[n],Helse_add_ch_list,copy=False)
# Fix channel info (both after removal of mastoids and newly added chs)
# Ch info loc are linked for all reference channels and this link is removed
for c in range(HELSE_final_epochs[n].info["nchan"]):
HELSE_final_epochs[n].info["chs"][c]["loc"] = HELSE_final_epochs[n].info["chs"][c]["loc"].copy()
HELSE_final_epochs[n].info["chs"][c]["scanno"] = c+1
HELSE_final_epochs[n].info["chs"][c]["logno"] = c+1
# Set new combined montage
HELSE_final_epochs[n].set_montage(final_digmon)
# Set newly added channels as "bad" and interpolate
HELSE_final_epochs[n].info["bads"] = Helse_add_ch_list
HELSE_final_epochs[n].interpolate_bads(reset_bads=True)
# Fix "picks" in order to reorder channels
HELSE_final_epochs[n].picks = np.array(range(HELSE_final_epochs[n].info["nchan"]))
# Reorder channels
HELSE_final_epochs[n].reorder_channels(final_ch_list)
# Add missing channels to Baseline and interpolate
Base_add_ch_list = list(set(final_ch_list)-set(Base_chs))
for n in range(len(Base_final_epochs)):
# Add empty channels to interpolate
mne.add_reference_channels(Base_final_epochs[n],Base_add_ch_list,copy=False)
# Fix channel info (both after removal of mastoids and newly added chs)
# Ch info loc are linked for all reference channels and this link is removed
for c in range(Base_final_epochs[n].info["nchan"]):
Base_final_epochs[n].info["chs"][c]["loc"] = Base_final_epochs[n].info["chs"][c]["loc"].copy()
Base_final_epochs[n].info["chs"][c]["scanno"] = c+1
Base_final_epochs[n].info["chs"][c]["logno"] = c+1
# Set new combined montage
Base_final_epochs[n].set_montage(final_digmon)
# Set newly added channels as "bad" and interpolate
Base_final_epochs[n].info["bads"] = Base_add_ch_list
Base_final_epochs[n].interpolate_bads(reset_bads=True)
# Fix "picks" in order to reorder channels
Base_final_epochs[n].picks = np.array(range(Base_final_epochs[n].info["nchan"]))
# Reorder channels
Base_final_epochs[n].reorder_channels(final_ch_list)
# Combine both dataset in one list
final_epochs = ISAF7_final_epochs+HELSE_final_epochs+Base_final_epochs
# Check number of epochs
file_lengths = [0]*len(final_epochs)
for i in range(len(final_epochs)):
file_lengths[i] = len(final_epochs[i])
# sns.distplot(file_lengths) # visualize
np.min(file_lengths)/150*100 # Max 20% epochs dropped. Above and the subjects were excluded
n_subjects = len(final_epochs)
# Re-sample files to 200Hz (Data was already lowpass filtered at 100, so above 200 is oversampling)
for i in range(len(final_epochs)):
final_epochs[i].resample(sfreq=200, verbose=2)
# # Save final epochs data
# save_path = "/home/glia/Analysis/Final_epochs_data"
# for n in range(len(final_epochs)):
# # Make file writeable
# final_epochs[n]._times_readonly.flags["WRITEABLE"] = False
# # Save file
# final_epochs[n].save(fname = os.path.join(save_path,str("{}_final_epoch".format(Subject_id[n]) + "-epo.fif")),
# overwrite=True, verbose=0)
# Load dropped epochs - used for gap idx in microstates
ISAF7_dropped_epochs_df = pd.read_pickle("ISAF7_dropped_epochs.pkl")
Helse_dropped_epochs_df = pd.read_pickle("HELSE_dropped_epochs.pkl")
Base_dropped_epochs_df = pd.read_pickle("Base_dropped_epochs.pkl")
Drop_epochs_df = pd.concat([ISAF7_dropped_epochs_df,Helse_dropped_epochs_df,
Base_dropped_epochs_df]).reset_index(drop=True)
good_subject_df_idx = [not i in bad_subjects for i in Drop_epochs_df["Subject_ID"]]
Drop_epochs_df = Drop_epochs_df.loc[good_subject_df_idx,:]
Drop_epochs_df = Drop_epochs_df.sort_values(by=["Subject_ID"]).reset_index(drop=True)
### Load questionnaire data
# ISAF
qdf_ISAF7 = pd.read_csv("/data/raw/FOR_DTU/Questionnaires_for_DTU.csv",
na_values=' ')
# Rename Subject_ID column
qdf_ISAF7.rename({"ID_number": "Subject_ID"}, axis=1, inplace=True)
# Sort Subject_id to match Subject_id for files
qdf_ISAF7 = qdf_ISAF7.sort_values(by=["Subject_ID"], ignore_index=True)
# Get column idx for PCL_t7 columns
PCL_idx = qdf_ISAF7.columns.str.contains("PCL") & np.invert(qdf_ISAF7.columns.str.contains("PCL_"))
# Keep subject id
PCL_idx[qdf_ISAF7.columns=="Subject_ID"] = True
# Make a final df and exclude dropped subjects
final_qdf0 = qdf_ISAF7.loc[qdf_ISAF7["Subject_ID"].isin(Subject_id),PCL_idx].reset_index(drop=True)
# Make column that is sum of all PCL
final_qdf0.insert(len(final_qdf0.columns),"PCL_total",np.sum(final_qdf0.iloc[:,1:],axis=1))
# Helse
qdf_helse = pd.read_csv("/data/may2020/Questionnaires/HelsfondenQuestData_nytLbn.csv",
sep=",", na_values=' ')
# Rename subject ID column
qdf_helse.rename(columns={"Nyt_lbn":"Subject_ID"}, inplace=True)
Helse_ID_modifier = 200000
# Add 200000 to id
qdf_helse["Subject_ID"] += Helse_ID_modifier
# Sort Subject_id to match Subject_id for files
qdf_helse = qdf_helse.sort_values(by=["Subject_ID"], ignore_index=True)
# Get column idx for PCL columns (don't use summarized columns with _)
PCL_idx = qdf_helse.columns.str.contains("PCL") & np.invert(qdf_helse.columns.str.contains("PCL_"))
# Keep subject id
PCL_idx[qdf_helse.columns=="Subject_ID"] = True
# Make a final df and exclude dropped subjects
final_qdf1 = qdf_helse.loc[qdf_helse["Subject_ID"].isin(Subject_id),PCL_idx].reset_index(drop=True)
# Make column that is sum of all PCL
final_qdf1.insert(len(final_qdf1.columns),"PCL_total",np.sum(final_qdf1.iloc[:,1:],axis=1))
# Baseline
# antal_børm renamed to antal_boern
qdf_base = pd.read_csv("/data/sep2020/BaselineForLi.csv", sep=",", na_values=' ')
# Rename subject ID column
qdf_base.rename(columns={"LbnRand":"Subject_ID"}, inplace=True)
Base_ID_modifier = 300000
# Add 300000 to id
qdf_base["Subject_ID"] += Base_ID_modifier
# Sort Subject_id to match Subject_id for files
qdf_base = qdf_base.sort_values(by=["Subject_ID"], ignore_index=True)
# Get column idx for PCL columns (don't use summarized columns with _)
PCL_idx = qdf_base.columns.str.contains("PCL") & np.invert(qdf_base.columns.str.contains("PCL_"))
# Keep subject id
PCL_idx[qdf_base.columns=="Subject_ID"] = True
# Make a final df and exclude dropped subjects
final_qdf2 = qdf_base.loc[qdf_base["Subject_ID"].isin(Subject_id),PCL_idx].reset_index(drop=True)
# Make column that is sum of all PCL
final_qdf2.insert(len(final_qdf2.columns),"PCL_total",np.sum(final_qdf2.iloc[:,1:],axis=1))
# Find NaN
nan_idx = np.where(final_qdf2.isna()==True)
final_qdf2.iloc[nan_idx[0],np.concatenate([np.array([0]),nan_idx[1]])] # 2252 has NaN for PCL3 and 12
# Interpolate with mean of column
final_qdf2 = final_qdf2.fillna(final_qdf2.mean())
# Combine the 3 datasets
final_qdf0.columns = final_qdf1.columns # fix colnames with t7
final_qdf = pd.concat([final_qdf0,final_qdf1,final_qdf2], ignore_index=True)
# Define folder for saving features
Feature_savepath = "./Features/"
Stat_savepath = "./Statistics/"
Model_savepath = "./Model/"
# Ensure all columns are integers
final_qdf = final_qdf.astype("int")
final_qdf.to_pickle(os.path.join(Feature_savepath,"final_qdf.pkl"))
# Define cases as >= 44 total PCL
# Type: numpy array with subject id
cases = np.array(final_qdf["Subject_ID"][final_qdf["PCL_total"]>=44])
n_groups = 2
Groups = ["CTRL", "PTSD"]
# Check percentage of cases in both datasets
len(np.where((cases>100000)&(cases<200000))[0])/n_ISAF # around 32%
len(np.where((cases>200000)&(cases<300000))[0])/n_HELSE # around 51%
len(np.where((cases>300000)&(cases<400000))[0])/n_Base # around 66%
# There is clearly class imbalance between studies!
### Get depression scores as binary above threshold
# BDI >= 20 is moderate depression
# DASS-42 >= 14 is moderate depression for depression subscale
dep_cases = np.concatenate([np.array(qdf_ISAF7["Subject_ID"][qdf_ISAF7["BDI_t7"] >= 20]),
np.array(qdf_helse["Subject_ID"][qdf_helse["DASS_D_t0"] >= 14]),
np.array(qdf_base["Subject_ID"][qdf_base["DASS_D_t0"] >= 14])])
dep_cases.sort()
dep_cases = dep_cases[np.isin(dep_cases,Subject_id)] # only keep those that we received and not excluded
# Check percentage of dep cases in both datasets
len(np.where((dep_cases>100000)&(dep_cases<200000))[0])/n_ISAF # around 34%
len(np.where((dep_cases>200000)&(dep_cases<300000))[0])/n_HELSE # around 53%
len(np.where((dep_cases>300000)&(dep_cases<400000))[0])/n_Base # around 72%
# Make normalized to max depression score to combine from both scales
# Relative score seem to be consistent between BDI-II and DASS-42 and clinical label
max_BDI = 63
max_DASS = 42
# Get normalized depression scores for each dataset
ISAF7_dep_score = qdf_ISAF7["BDI_t7"][qdf_ISAF7["Subject_ID"].isin(Subject_id)]/max_BDI
Helse_dep_score = qdf_helse["DASS_D_t0"][qdf_helse["Subject_ID"].isin(Subject_id)]/max_DASS
Base_dep_score = qdf_base["DASS_D_t0"][qdf_base["Subject_ID"].isin(Subject_id)]/max_DASS
Norm_dep_score = np.concatenate([ISAF7_dep_score.to_numpy(),Helse_dep_score.to_numpy(),Base_dep_score.to_numpy()])
# Check if subject id match when using concat
test_d1 = qdf_ISAF7["Subject_ID"][qdf_ISAF7["Subject_ID"].isin(Subject_id)]
test_d2 = qdf_helse["Subject_ID"][qdf_helse["Subject_ID"].isin(Subject_id)]
test_d3 = qdf_base["Subject_ID"][qdf_base["Subject_ID"].isin(Subject_id)]
test_d4 = np.concatenate([test_d1.to_numpy(),test_d2.to_numpy(),test_d3.to_numpy()])
assert all(np.equal(Subject_id,test_d4))
# %% Power spectrum features
Freq_Bands = {"delta": [1.25, 4.0],
"theta": [4.0, 8.0],
"alpha": [8.0, 13.0],
"beta": [13.0, 30.0],
"gamma": [30.0, 49.0]}
ch_names = final_epochs[0].info["ch_names"]
n_channels = final_epochs[0].info["nchan"]
# Pre-allocate memory
power_bands = [0]*len(final_epochs)
def power_band_estimation(n):
# Get index for eyes open and eyes closed
EC_index = final_epochs[n].events[:,2] == 1
EO_index = final_epochs[n].events[:,2] == 2
# Calculate the power spectral density
psds, freqs = psd_multitaper(final_epochs[n], fmin = 1, fmax = 50) # output (epochs, channels, freqs)
temp_power_band = []
for fmin, fmax in Freq_Bands.values():
# Calculate the power each frequency band
psds_band = psds[:, :, (freqs >= fmin) & (freqs < fmax)].sum(axis=-1)
# Calculate the mean for each eye status
psds_band_eye = np.array([psds_band[EC_index,:].mean(axis=0),
psds_band[EO_index,:].mean(axis=0)])
# Append for each freq band
temp_power_band.append(psds_band_eye)
# Output: List with the 5 bands, and each element is a 2D array with eye status as 1st dimension and channels as 2nd dimension
# The list is reshaped and absolute and relative power are calculated
abs_power_band = np.reshape(temp_power_band, (5, 2, n_channels))
abs_power_band = 10.*np.log10(abs_power_band) # Convert to decibel scale
rel_power_band = np.reshape(temp_power_band, (5, 2, n_channels))
rel_power_band = rel_power_band/np.sum(rel_power_band, axis=0, keepdims=True)
# each eye condition and channel have been normalized to power in all 5 frequencies for that given eye condition and channel
# Make one list in 1 dimension
abs_power_values = np.concatenate(np.concatenate(abs_power_band, axis=0), axis=0)
rel_power_values = np.concatenate(np.concatenate(rel_power_band, axis=0), axis=0)
## Output: First the channels, then the eye status and finally the frequency bands are concatenated
## E.g. element 26 is 3rd channel, eyes open, first frequency band
#assert abs_power_values[26] == abs_power_band[0,1,2]
#assert abs_power_values[47] == abs_power_band[0,1,23] # +21 channels to last
#assert abs_power_values[50] == abs_power_band[1,0,2] # once all channels have been changed then the freq is changed and eye status
# Get result
res = np.concatenate([abs_power_values,rel_power_values],axis=0)
return n, res
"""
with concurrent.futures.ProcessPoolExecutor() as executor:
for n, result in executor.map(power_band_estimation, range(len(final_epochs))): # Function and arguments
power_bands[n] = result
"""
"""
for i in range(len(power_bands)):
n, results = power_band_estimation(i)
power_bands[i] = results
# Combine all data into one dataframe
# First the columns are prepared
n_subjects = len(Subject_id)
# The group status (PTSD/CTRL) is made using the information about the cases
Group_status = np.array(["CTRL"]*n_subjects)
Group_status[np.array([i in cases for i in Subject_id])] = "PTSD"
# A variable that codes the channels based on A/P localization is also made
Frontal_chs = ["Fp1", "Fpz", "Fp2", "AFz", "Fz", "F3", "F4", "F7", "F8"]
Central_chs = ["Cz", "C3", "C4", "T7", "T8", "FT7", "FC3", "FCz", "FC4", "FT8", "TP7", "CP3", "CPz", "CP4", "TP8"]
Posterior_chs = ["Pz", "P3", "P4", "P7", "P8", "POz", "O1", "O2", "Oz"]
Parietal_chs = ["TP7", "CP3", "CPz", "CP4", "TP8", "P7", "P3", "Pz", "P4", "P8", "POz"]
Brain_region_labels = ["Frontal","Central","Posterior","Parietal"]
Brain_region = np.array(ch_names, dtype = "<U9")
Brain_region[np.array([i in Frontal_chs for i in ch_names])] = Brain_region_labels[0]
Brain_region[np.array([i in Central_chs for i in ch_names])] = Brain_region_labels[1]
Brain_region[np.array([i in Posterior_chs for i in ch_names])] = Brain_region_labels[2]
Brain_region[np.array([i in Parietal_chs for i in ch_names])] = Brain_region_labels[3]
# A variable that codes the channels based on M/L localization
Left_chs = ["Fp1", "F3", "F7", "FC3", "FT7", "C3", "T7", "CP3", "TP7", "P3", "P7", "O1"]
Right_chs = ["Fp2", "F4", "F8", "FC4", "FT8", "C4", "T8", "CP4", "TP8", "P4", "P8", "O2"]
Mid_chs = ["Fpz", "AFz", "Fz", "FCz", "Cz", "CPz", "Pz", "POz", "Oz"]
Brain_side = np.array(ch_names, dtype = "<U5")
Brain_side[np.array([i in Left_chs for i in ch_names])] = "Left"
Brain_side[np.array([i in Right_chs for i in ch_names])] = "Right"
Brain_side[np.array([i in Mid_chs for i in ch_names])] = "Mid"
# Eye status is added
eye_status = list(final_epochs[0].event_id.keys())
n_eye_status = len(eye_status)
# Frequency bands
freq_bands_name = list(Freq_Bands.keys())
n_freq_bands = len(freq_bands_name)
# Quantification (Abs/Rel)
quant_status = ["Absolute", "Relative"]
n_quant_status = len(quant_status)
# The dataframe is made by combining all the unlisted pds values
# Each row correspond to a different channel. It is reset after all channel names have been used
# Each eye status element is repeated n_channel times, before it is reset
# Each freq_band element is repeated n_channel * n_eye_status times, before it is reset
# Each quantification status element is repeated n_channel * n_eye_status * n_freq_bands times, before it is reset
power_df = pd.DataFrame(data = {"Subject_ID": [ele for ele in Subject_id for i in range(n_eye_status*n_quant_status*n_freq_bands*n_channels)],
"Group_status": [ele for ele in Group_status for i in range(n_eye_status*n_quant_status*n_freq_bands*n_channels)],
"Channel": ch_names*(n_eye_status*n_quant_status*n_freq_bands*n_subjects),
"Brain_region": list(Brain_region)*(n_eye_status*n_quant_status*n_freq_bands*n_subjects),
"Brain_side": list(Brain_side)*(n_eye_status*n_quant_status*n_freq_bands*n_subjects),
"Eye_status": [ele for ele in eye_status for i in range(n_channels)]*n_quant_status*n_freq_bands*n_subjects,
"Freq_band": [ele for ele in freq_bands_name for i in range(n_channels*n_eye_status)]*n_quant_status*n_subjects,
"Quant_status": [ele for ele in quant_status for i in range(n_channels*n_eye_status*n_freq_bands)]*n_subjects,
"PSD": list(np.concatenate(power_bands, axis=0))
})
# Absolute power is in decibels (10*log10(power))
# Fix Freq_band categorical order
power_df["Freq_band"] = power_df["Freq_band"].astype("category").\
cat.reorder_categories(list(Freq_Bands.keys()), ordered=True)
# Fix Brain_region categorical order
power_df["Brain_region"] = power_df["Brain_region"].astype("category").\
cat.reorder_categories(Brain_region_labels, ordered=True)
"""
# Save the dataframe
# power_df.to_pickle(os.path.join(Feature_savepath,"Power_df.pkl"))
"""
# %% Theta-beta ratio
# Frontal theta/beta ratio has been implicated in cognitive control of attention
power_df = pd.read_pickle(os.path.join(Feature_savepath,"Power_df.pkl"))
eye_status = list(final_epochs[0].event_id)
n_eye_status = len(eye_status)
# Subset frontal absolute power
power_df_sub1 = power_df[(power_df["Quant_status"] == "Absolute")&
(power_df["Brain_region"] == "Frontal")]
# Subset frontal, midline absolute power
power_df_sub2 = power_df[(power_df["Quant_status"] == "Absolute")&
(power_df["Brain_region"] == "Frontal")&
(power_df["Brain_side"] == "Mid")]
# Subset posterior absolute power
power_df_sub3 = power_df[(power_df["Quant_status"] == "Absolute")&
(power_df["Brain_region"] == "Posterior")]
# Calculate average frontal power theta
frontal_theta_mean_subject = power_df_sub1[power_df_sub1["Freq_band"] == "theta"].\
groupby(["Subject_ID","Group_status","Eye_status"]).mean().reset_index()
# Calculate average frontal power beta
frontal_beta_mean_subject = power_df_sub1[power_df_sub1["Freq_band"] == "beta"].\
groupby(["Subject_ID","Group_status","Eye_status"]).mean().reset_index()
# Extract all values
frontal_beta_subject_values = power_df_sub1[power_df_sub1["Freq_band"] == "beta"]
# Calculate average frontal, midline power theta
frontal_midline_theta_mean_subject = power_df_sub2[power_df_sub2["Freq_band"] == "theta"].\
groupby(["Subject_ID","Group_status","Eye_status"]).mean().reset_index()
# Extract all values
frontal_midline_theta_subject_values = power_df_sub2[power_df_sub2["Freq_band"] == "theta"]
# Calculate average parietal alpha power
parietal_alpha_mean_subject = power_df_sub3[power_df_sub3["Freq_band"] == "alpha"].\
groupby(["Subject_ID","Group_status","Eye_status"]).mean().reset_index()
# Extract all values
parietal_alpha_subject_values = power_df_sub3[power_df_sub3["Freq_band"] == "alpha"]
# Convert from dB to raw power
frontal_theta_mean_subject["PSD"] = 10**(frontal_theta_mean_subject["PSD"]/10)
frontal_beta_mean_subject["PSD"] = 10**(frontal_beta_mean_subject["PSD"]/10)
frontal_midline_theta_mean_subject["PSD"] = 10**(frontal_midline_theta_mean_subject["PSD"]/10)
frontal_beta_subject_values["PSD"] = 10**(frontal_beta_subject_values["PSD"]/10)
frontal_midline_theta_subject_values["PSD"] = 10**(frontal_midline_theta_subject_values["PSD"]/10)
parietal_alpha_mean_subject["PSD"] = 10**(parietal_alpha_mean_subject["PSD"]/10)
parietal_alpha_subject_values["PSD"] = 10**(parietal_alpha_subject_values["PSD"]/10)
# Safe values
frontal_beta_mean_subject.to_pickle(os.path.join(Feature_savepath,"fBMS_df.pkl"))
frontal_midline_theta_mean_subject.to_pickle(os.path.join(Feature_savepath,"fMTMS_df.pkl"))
frontal_beta_subject_values.to_pickle(os.path.join(Feature_savepath,"fBSV_df.pkl"))
frontal_midline_theta_subject_values.to_pickle(os.path.join(Feature_savepath,"fMTSV_df.pkl"))
parietal_alpha_mean_subject.to_pickle(os.path.join(Feature_savepath,"pAMS_df.pkl"))
parietal_alpha_subject_values.to_pickle(os.path.join(Feature_savepath,"pASV_df.pkl"))
# Calculate mean for each group and take ratio for whole group
# To confirm trend observed in PSD plots
mean_group_f_theta = frontal_theta_mean_subject.iloc[:,1:].groupby(["Group_status","Eye_status"]).mean()
mean_group_f_beta = frontal_beta_mean_subject.iloc[:,1:].groupby(["Group_status","Eye_status"]).mean()
mean_group_f_theta_beta_ratio = mean_group_f_theta/mean_group_f_beta
# Calculate ratio for each subject
frontal_theta_beta_ratio = frontal_theta_mean_subject.copy()
frontal_theta_beta_ratio["PSD"] = frontal_theta_mean_subject["PSD"]/frontal_beta_mean_subject["PSD"]
# Take the natural log of ratio
frontal_theta_beta_ratio["PSD"] = np.log(frontal_theta_beta_ratio["PSD"])
# Rename and save feature
frontal_theta_beta_ratio.rename(columns={"PSD":"TBR"},inplace=True)
# Add dummy variable for re-using plot code
dummy_variable = ["Frontal Theta Beta Ratio"]*frontal_theta_beta_ratio.shape[0]
frontal_theta_beta_ratio.insert(3, "Measurement", dummy_variable )
# frontal_theta_beta_ratio.to_pickle(os.path.join(Feature_savepath,"fTBR_df.pkl"))
# %% Frequency bands asymmetry
# Defined as ln(right) - ln(left)
# Thus we should only work with the absolute values and undo the dB transformation
# Here I avg over all areas. I.e. mean((ln(F4)-ln(F3),(ln(F8)-ln(F7),(ln(Fp2)-ln(Fp1))) for frontal
ROI = ["Frontal", "Central", "Posterior"]
qq = "Absolute" # only calculate asymmetry for absolute
# Pre-allocate memory
asymmetry = np.zeros(shape=(len(np.unique(power_df["Subject_ID"])),
len(np.unique(power_df["Eye_status"])),
len(list(Freq_Bands.keys())),
len(ROI)))
def calculate_asymmetry(i):
ii = np.unique(power_df["Subject_ID"])[i]
temp_asymmetry = np.zeros(shape=(len(np.unique(power_df["Eye_status"])),
len(list(Freq_Bands.keys())),
len(ROI)))
for e in range(len(np.unique(power_df["Eye_status"]))):
ee = np.unique(power_df["Eye_status"])[e]
for f in range(len(list(Freq_Bands.keys()))):
ff = list(Freq_Bands.keys())[f]
# Get the specific part of the df
temp_power_df = power_df[(power_df["Quant_status"] == qq) &
(power_df["Eye_status"] == ee) &
(power_df["Subject_ID"] == ii) &
(power_df["Freq_band"] == ff)].copy()
# Convert from dB to raw power
temp_power_df.loc[:,"PSD"] = np.array(10**(temp_power_df["PSD"]/10))
# Calculate the power asymmetry
for r in range(len(ROI)):
rr = ROI[r]
temp_power_roi_df = temp_power_df[(temp_power_df["Brain_region"] == rr)&
~(temp_power_df["Brain_side"] == "Mid")]
# Sort using channel names to make sure F8-F7 and not F4-F7 etc.
temp_power_roi_df = temp_power_roi_df.sort_values("Channel").reset_index(drop=True)
# Get the log power
R_power = temp_power_roi_df[(temp_power_roi_df["Brain_side"] == "Right")]["PSD"]
ln_R_power = np.log(R_power) # get log power
L_power = temp_power_roi_df[(temp_power_roi_df["Brain_side"] == "Left")]["PSD"]
ln_L_power = np.log(L_power) # get log power
# Pairwise subtraction followed by averaging
asymmetry_value = np.mean(np.array(ln_R_power) - np.array(ln_L_power))
# Save it to the array
temp_asymmetry[e,f,r] = asymmetry_value
# Print progress
print("{} out of {} finished testing".format(i+1,n_subjects))
return i, temp_asymmetry
with concurrent.futures.ProcessPoolExecutor() as executor:
for i, res in executor.map(calculate_asymmetry, range(len(np.unique(power_df["Subject_ID"])))): # Function and arguments
asymmetry[i,:,:,:] = res
# Prepare conversion of array to df using flatten
n_subjects = len(Subject_id)
# The group status (PTSD/CTRL) is made using the information about the cases
Group_status = np.array(["CTRL"]*n_subjects)
Group_status[np.array([i in cases for i in Subject_id])] = "PTSD"
# Eye status is added
eye_status = list(final_epochs[0].event_id.keys())
n_eye_status = len(eye_status)
# Frequency bands
freq_bands_name = list(Freq_Bands.keys())
n_freq_bands = len(freq_bands_name)
# ROIs
n_ROI = len(ROI)
# Make the dataframe
asymmetry_df = pd.DataFrame(data = {"Subject_ID": [ele for ele in Subject_id for i in range(n_eye_status*n_freq_bands*n_ROI)],
"Group_status": [ele for ele in Group_status for i in range(n_eye_status*n_freq_bands*n_ROI)],
"Eye_status": [ele for ele in eye_status for i in range(n_freq_bands*n_ROI)]*(n_subjects),
"Freq_band": [ele for ele in freq_bands_name for i in range(n_ROI)]*(n_subjects*n_eye_status),
"ROI": list(ROI)*(n_subjects*n_eye_status*n_freq_bands),
"Asymmetry_score": asymmetry.flatten(order="C")
})
# Flatten with order=C means that it first goes through last axis,
# then repeat along 2nd last axis, and then repeat along 3rd last etc
# Asymmetry numpy to pandas conversion check
random_point=321
asymmetry_df.iloc[random_point]
i = np.where(np.unique(power_df["Subject_ID"]) == asymmetry_df.iloc[random_point]["Subject_ID"])[0]
e = np.where(np.unique(power_df["Eye_status"]) == asymmetry_df.iloc[random_point]["Eye_status"])[0]
f = np.where(np.array(list(Freq_Bands.keys())) == asymmetry_df.iloc[random_point]["Freq_band"])[0]
r = np.where(np.array(ROI) == asymmetry_df.iloc[random_point]["ROI"])[0]
assert asymmetry[i,e,f,r] == asymmetry_df.iloc[random_point]["Asymmetry_score"]
# Save the dataframe
asymmetry_df.to_pickle(os.path.join(Feature_savepath,"asymmetry_df.pkl"))
"""
"""
# %% Using FOOOF
# Peak alpha frequency (PAF) and 1/f exponent (OOF)
# Using the FOOOF algorithm (Fitting oscillations and one over f)
# Published by Donoghue et al, 2020 in Nature Neuroscience
# To start, FOOOF takes the freqs and power spectra as input
n_channels = final_epochs[0].info["nchan"]
ch_names = final_epochs[0].info["ch_names"]
sfreq = final_epochs[0].info["sfreq"]
Freq_Bands = {"delta": [1.25, 4.0],
"theta": [4.0, 8.0],
"alpha": [8.0, 13.0],
"beta": [13.0, 30.0],
"gamma": [30.0, 49.0]}
n_freq_bands = len(Freq_Bands)
# From visual inspection there seems to be problem if PSD is too steep at the start
# To overcome this problem, we try multiple start freq
OOF_r2_thres = 0.95 # a high threshold as we allow for overfitting
PAF_r2_thres = 0.90 # a more lenient threshold for PAF, as it is usually still captured even if fit for 1/f is not perfect
PTF_r2_thres = 0.90 # a more lenient threshold for PTF, as it is usually still captured even if fit for 1/f is not perfect
PBF_r2_thres = 0.90 # a more lenient threshold for PBF, as it is usually still captured even if fit for 1/f is not perfect
freq_start_it_range = [2,3,4,5,6]
freq_end = 40 # Stop freq at 40Hz to not be influenced by the Notch Filter
eye_status = list(final_epochs[0].event_id.keys())
n_eye_status = len(eye_status)
PAF_data = np.zeros((n_subjects,n_eye_status,n_channels,3)) # CF, power, band_width
PTF_data = np.zeros((n_subjects,n_eye_status,n_channels,3)) # CF, power, band_width
PBF_data = np.zeros((n_subjects,n_eye_status,n_channels,3)) # CF, power, band_width
OOF_data = np.zeros((n_subjects,n_eye_status,n_channels,2)) # offset and exponent
def FOOOF_estimation(i):
PAF_data0 = np.zeros((n_eye_status,n_channels,3)) # CF, power, band_width
PTF_data0 = np.zeros((n_eye_status,n_channels,3)) # CF, power, band_width
PBF_data0 = np.zeros((n_eye_status,n_channels,3)) # CF, power, band_width
OOF_data0 = np.zeros((n_eye_status,n_channels,2)) # offset and exponent
# Get Eye status
eye_idx = [final_epochs[i].events[:,2] == 1, final_epochs[i].events[:,2] == 2] # EC and EO
# Calculate the power spectral density
psd, freqs = psd_multitaper(final_epochs[i], fmin = 1, fmax = 50) # output (epochs, channels, freqs)
# Retrieve psds for the 2 conditions and calculate mean across epochs
psds = []
for e in range(n_eye_status):
# Get the epochs for specific eye condition
temp_psd = psd[eye_idx[e],:,:]
# Calculate the mean across epochs
temp_psd = np.mean(temp_psd, axis=0)
# Save
psds.append(temp_psd)
# Try multiple start freq
PAF_data00 = np.zeros((n_eye_status,n_channels,len(freq_start_it_range),3)) # CF, power, band_width
PTF_data00 = np.zeros((n_eye_status,n_channels,len(freq_start_it_range),3)) # CF, power, band_width
PBF_data00 = np.zeros((n_eye_status,n_channels,len(freq_start_it_range),3)) # CF, power, band_width
OOF_data00 = np.zeros((n_eye_status,n_channels,len(freq_start_it_range),2)) # offset and exponent
r2s00 = np.zeros((n_eye_status,n_channels,len(freq_start_it_range)))
for e in range(n_eye_status):
psds_avg = psds[e]
for f in range(len(freq_start_it_range)):
# Initiate FOOOF group for analysis of multiple PSD
fg = fooof.FOOOFGroup()
# Set the frequency range to fit the model
freq_range = [freq_start_it_range[f], freq_end] # variable freq start to 49Hz
# Fit to each source PSD separately, but in parallel
fg.fit(freqs,psds_avg,freq_range,n_jobs=1)
# Extract aperiodic parameters
aps = fg.get_params('aperiodic_params')
# Extract peak parameters
peaks = fg.get_params('peak_params')
# Extract goodness-of-fit metrics
r2s = fg.get_params('r_squared')
# Save OOF and r2s
OOF_data00[e,:,f] = aps
r2s00[e,:,f] = r2s
# Find the alpha peak with greatest power
for c in range(n_channels):
peaks0 = peaks[peaks[:,3] == c]
# Subset the peaks within the alpha band
in_alpha_band = (peaks0[:,0] >= Freq_Bands["alpha"][0]) & (peaks0[:,0] <= Freq_Bands["alpha"][1])
if sum(in_alpha_band) > 0: # Any alpha peaks?
# Choose the peak with the highest power
max_alpha_idx = np.argmax(peaks0[in_alpha_band,1])
# Save results
PAF_data00[e,c,f] = peaks0[in_alpha_band][max_alpha_idx,:-1]
else:
# No alpha peaks
PAF_data00[e,c,f] = [np.nan]*3
# Find the theta peak with greatest power
for c in range(n_channels):
peaks0 = peaks[peaks[:,3] == c]
# Subset the peaks within the theta band
in_theta_band = (peaks0[:,0] >= Freq_Bands["theta"][0]) & (peaks0[:,0] <= Freq_Bands["theta"][1])
if sum(in_theta_band) > 0:
# Choose the peak with the highest power
max_theta_idx = np.argmax(peaks0[in_theta_band,1])
# Save results
PTF_data00[e,c,f] = peaks0[in_theta_band][max_theta_idx,:-1]
else:
# No theta peaks
PTF_data00[e,c,f] = [np.nan]*3
# Find the beta peak with greatest power
for c in range(n_channels):
peaks0 = peaks[peaks[:,3] == c]
# Subset the peaks within the beta band
in_beta_band = (peaks0[:,0] >= Freq_Bands["beta"][0]) & (peaks0[:,0] <= Freq_Bands["beta"][1])
if sum(in_beta_band) > 0:
# Choose the peak with the highest power
max_beta_idx = np.argmax(peaks0[in_beta_band,1])
# Save results
PBF_data00[e,c,f] = peaks0[in_beta_band][max_beta_idx,:-1]
else:
# No beta peaks
PBF_data00[e,c,f] = [np.nan]*3
# Check criterias
good_fits_OOF = (r2s00 > OOF_r2_thres) & (OOF_data00[:,:,:,1] > 0) # r^2 > 0.95 and exponent > 0
good_fits_PAF = (r2s00 > PAF_r2_thres) & (np.isfinite(PAF_data00[:,:,:,0])) # r^2 > 0.90 and detected peak in alpha band
good_fits_PTF = (r2s00 > PTF_r2_thres) & (np.isfinite(PTF_data00[:,:,:,0])) # r^2 > 0.90 and detected peak in theta band
good_fits_PBF = (r2s00 > PBF_r2_thres) & (np.isfinite(PBF_data00[:,:,:,0])) # r^2 > 0.90 and detected peak in beta band
# Save the data or NaN if criterias were not fulfilled
for e in range(n_eye_status):
for c in range(n_channels):
if sum(good_fits_OOF[e,c]) == 0: # no good OOF estimation
OOF_data0[e,c] = [np.nan]*2
else: # Save OOF associated with greatest r^2 that fulfilled criterias
OOF_data0[e,c] = OOF_data00[e,c,np.argmax(r2s00[e,c,good_fits_OOF[e,c]])]
if sum(good_fits_PAF[e,c]) == 0: # no good PAF estimation
PAF_data0[e,c] = [np.nan]*3
else: # Save PAF associated with greatest r^2 that fulfilled criterias
PAF_data0[e,c] = PAF_data00[e,c,np.argmax(r2s00[e,c,good_fits_PAF[e,c]])]
if sum(good_fits_PTF[e,c]) == 0: # no good PTF estimation
PTF_data0[e,c] = [np.nan]*3
else: # Save PTF associated with greatest r^2 that fulfilled criterias
PTF_data0[e,c] = PTF_data00[e,c,np.argmax(r2s00[e,c,good_fits_PTF[e,c]])]
if sum(good_fits_PBF[e,c]) == 0: # no good PBF estimation
PBF_data0[e,c] = [np.nan]*3
else: # Save PBF associated with greatest r^2 that fulfilled criterias
PBF_data0[e,c] = PBF_data00[e,c,np.argmax(r2s00[e,c,good_fits_PBF[e,c]])]
print("Finished {} out of {} subjects".format(i+1,n_subjects))
return i, PAF_data0, OOF_data0, PTF_data0, PBF_data0
# Get current time
c_time1 = time.localtime()
c_time1 = time.strftime("%a %d %b %Y %H:%M:%S", c_time1)
print(c_time1)
# with concurrent.futures.ProcessPoolExecutor() as executor:
# for i, PAF_result, OOF_result in executor.map(FOOOF_estimation, range(n_subjects)): # Function and arguments
# PAF_data[i] = PAF_result
# OOF_data[i] = OOF_result
for i in range(n_subjects):
j, PAF_result, OOF_result, PTF_data0, PBF_data0 = FOOOF_estimation(i) # Function and arguments
PAF_data[i] = PAF_result
OOF_data[i] = OOF_result
PTF_data[i] = PTF_data0
PBF_data[i] = PBF_data0
# Save data
with open(Feature_savepath+"PAF_data_arr.pkl", "wb") as file:
pickle.dump(PAF_data, file)
with open(Feature_savepath+"PTF_data_arr.pkl", "wb") as file:
pickle.dump(PTF_data, file)
with open(Feature_savepath+"PBF_data_arr.pkl", "wb") as file:
pickle.dump(PBF_data, file)
# with open(Feature_savepath+"OOF_data_arr.pkl", "wb") as file:
# pickle.dump(OOF_data, file)
# Get current time
c_time2 = time.localtime()
c_time2 = time.strftime("%a %d %b %Y %H:%M:%S", c_time2)
print("Started", c_time1, "\nFinished",c_time2)
# Convert to Pandas dataframe (only keep mean parameter for PAF)
# The dimensions will each be a column with numbers and the last column will be the actual values
ori = PAF_data[:,:,:,0]
ori_2 = PTF_data[:,:,:,0]
ori_3 = PBF_data[:,:,:,0]
arr = np.column_stack(list(map(np.ravel, np.meshgrid(*map(np.arange, ori.shape), indexing="ij"))) + [ori.ravel()])
arr_2 = np.column_stack(list(map(np.ravel, np.meshgrid(*map(np.arange, ori_2.shape), indexing="ij"))) + [ori_2.ravel()])
arr_3 = np.column_stack(list(map(np.ravel, np.meshgrid(*map(np.arange, ori_3.shape), indexing="ij"))) + [ori_3.ravel()])
PAF_data_df = pd.DataFrame(arr, columns = ["Subject_ID", "Eye_status", "Channel", "Value"])
PTF_data_df = pd.DataFrame(arr_2, columns = ["Subject_ID", "Eye_status", "Channel", "Value"])
PBF_data_df = pd.DataFrame(arr_3, columns = ["Subject_ID", "Eye_status", "Channel", "Value"])
# Change from numerical coding to actual values
index_values = [Subject_id,eye_status,ch_names]
temp_df = PAF_data_df.copy() # make temp df to not sequential overwrite what is changed
temp_df_2 = PTF_data_df.copy() # make temp df to not sequential overwrite what is changed
temp_df_3 = PBF_data_df.copy() # make temp df to not sequential overwrite what is changed
for col in range(len(index_values)):
col_name = PAF_data_df.columns[col]
col_name_2 = PTF_data_df.columns[col]
col_name_3 = PBF_data_df.columns[col]
for shape in range(ori.shape[col]):
temp_df.loc[PAF_data_df.iloc[:,col] == shape,col_name]\
= index_values[col][shape]
temp_df_2.loc[PTF_data_df.iloc[:,col] == shape,col_name_2]\
= index_values[col][shape]
temp_df_3.loc[PBF_data_df.iloc[:,col] == shape,col_name_3]\
= index_values[col][shape]
PAF_data_df = temp_df # replace original df
PTF_data_df = temp_df_2 # replace original df
PBF_data_df = temp_df_3 # replace original df
# Add group status
Group_status = np.array(["CTRL"]*len(PAF_data_df["Subject_ID"]))
Group_status[np.array([i in cases for i in PAF_data_df["Subject_ID"]])] = "PTSD"
# Add to dataframe
PAF_data_df.insert(3, "Group_status", Group_status)
PTF_data_df.insert(3, "Group_status", Group_status)
PBF_data_df.insert(3, "Group_status", Group_status)
# Global peak alpha
PAF_data_df_global = PAF_data_df.groupby(["Subject_ID", "Group_status", "Eye_status"]).mean().reset_index() # by default pandas mean skip nan
PTF_data_df_global = PTF_data_df.groupby(["Subject_ID", "Group_status", "Eye_status"]).mean().reset_index() # by default pandas mean skip nan
PBF_data_df_global = PBF_data_df.groupby(["Subject_ID", "Group_status", "Eye_status"]).mean().reset_index() # by default pandas mean skip nan
# Add dummy variable for re-using plot code
dummy_variable = ["Global Peak Alpha Frequency"]*PAF_data_df_global.shape[0]
dummy_variable_2 = ["Global Peak Theta Frequency"]*PTF_data_df_global.shape[0]
dummy_variable_3 = ["Global Peak Beta Frequency"]*PBF_data_df_global.shape[0]
PAF_data_df_global.insert(3, "Measurement", dummy_variable )
PTF_data_df_global.insert(3, "Measurement", dummy_variable_2 )
PBF_data_df_global.insert(3, "Measurement", dummy_variable_3 )
# Regional peak alpha
# A variable that codes the channels based on A/P localization is also made
Frontal_chs = ["Fp1", "Fpz", "Fp2", "AFz", "Fz", "F3", "F4", "F7", "F8"]
Central_chs = ["Cz", "C3", "C4", "T7", "T8", "FT7", "FC3", "FCz", "FC4", "FT8", "TP7", "CP3", "CPz", "CP4", "TP8"]
Posterior_chs = ["Pz", "P3", "P4", "P7", "P8", "POz", "O1", "O2", "Oz"]
Parietal_chs = ["TP7", "CP3", "CPz", "CP4", "TP8", "P7", "P3", "Pz", "P4", "P8", "POz"]
Brain_region_labels = ["Frontal","Central","Posterior","Parietal"]
Brain_region = np.array(ch_names, dtype = "<U9")
Brain_region[np.array([i in Frontal_chs for i in ch_names])] = Brain_region_labels[0]
Brain_region[np.array([i in Central_chs for i in ch_names])] = Brain_region_labels[1]
Brain_region[np.array([i in Posterior_chs for i in ch_names])] = Brain_region_labels[2]
Brain_region[np.array([i in Parietal_chs for i in ch_names])] = Brain_region_labels[3]
# Insert region type into dataframe
PAF_data_df.insert(4, "Brain_region", list(Brain_region)*int(PAF_data_df.shape[0]/len(Brain_region)))
PTF_data_df.insert(4, "Brain_region", list(Brain_region)*int(PTF_data_df.shape[0]/len(Brain_region)))
PBF_data_df.insert(4, "Brain_region", list(Brain_region)*int(PBF_data_df.shape[0]/len(Brain_region)))
# A variable that codes the channels based on M/L localization
Left_chs = ["Fp1", "F3", "F7", "FC3", "FT7", "C3", "T7", "CP3", "TP7", "P3", "P7", "O1"]
Right_chs = ["Fp2", "F4", "F8", "FC4", "FT8", "C4", "T8", "CP4", "TP8", "P4", "P8", "O2"]
Mid_chs = ["Fpz", "AFz", "Fz", "FCz", "Cz", "CPz", "Pz", "POz", "Oz"]
Brain_side = np.array(ch_names, dtype = "<U5")
Brain_side[np.array([i in Left_chs for i in ch_names])] = "Left"
Brain_side[np.array([i in Right_chs for i in ch_names])] = "Right"
Brain_side[np.array([i in Mid_chs for i in ch_names])] = "Mid"
# Insert side type into dataframe:
PAF_data_df.insert(5, "Brain_side", list(Brain_side)*int(PAF_data_df.shape[0]/len(Brain_side)))
PTF_data_df.insert(5, "Brain_side", list(Brain_side)*int(PTF_data_df.shape[0]/len(Brain_side)))
PBF_data_df.insert(5, "Brain_side", list(Brain_side)*int(PBF_data_df.shape[0]/len(Brain_side)))
# Define region of interest before saving
PAF_data_df = PAF_data_df[(PAF_data_df["Brain_region"] == "Parietal")] # Parietal region in peak alpha frequencys
PTF_data_df = PTF_data_df[(PTF_data_df["Brain_region"] == "Frontal") &
((PTF_data_df["Brain_side"] == "Mid"))] # Frontal midline theta peak frequencys
PBF_data_df = PBF_data_df[(PBF_data_df["Brain_region"] == "Frontal")] # Frontal beta peak frequencys
# Save the dataframes
PAF_data_df.to_pickle(os.path.join(Feature_savepath,"PAF_data_FOOOF_df.pkl"))
PAF_data_df_global.to_pickle(os.path.join(Feature_savepath,"PAF_data_FOOOF_global_df.pkl"))
PTF_data_df.to_pickle(os.path.join(Feature_savepath,"PTF_data_FOOOF_df.pkl"))
PTF_data_df_global.to_pickle(os.path.join(Feature_savepath,"PTF_data_FOOOF_global_df.pkl"))
PBF_data_df.to_pickle(os.path.join(Feature_savepath,"PBF_data_FOOOF_df.pkl"))
PBF_data_df_global.to_pickle(os.path.join(Feature_savepath,"PBF_data_FOOOF_global_df.pkl"))
"""
# # Convert to Pandas dataframe (only keep exponent parameter for OOF)
# # The dimensions will each be a column with numbers and the last column will be the actual values
# ori = OOF_data[:,:,:,1]
# arr = np.column_stack(list(map(np.ravel, np.meshgrid(*map(np.arange, ori.shape), indexing="ij"))) + [ori.ravel()])
# PAF_data_df = pd.DataFrame(arr, columns = ["Subject_ID", "Eye_status", "Channel", "Value"])
# # Change from numerical coding to actual values
# index_values = [Subject_id,eye_status,ch_names]
# temp_df = PAF_data_df.copy() # make temp df to not sequential overwrite what is changed
# for col in range(len(index_values)):
# col_name = PAF_data_df.columns[col]
# for shape in range(ori.shape[col]):
# temp_df.loc[PAF_data_df.iloc[:,col] == shape,col_name]\
# = index_values[col][shape]
# OOF_data_df = temp_df # replace original df
# # Add group status
# Group_status = np.array(["CTRL"]*len(OOF_data_df["Subject_ID"]))
# Group_status[np.array([i in cases for i in OOF_data_df["Subject_ID"]])] = "PTSD"
# # Add to dataframe
# OOF_data_df.insert(3, "Group_status", Group_status)
# # Regional OOF
# OOF_data_df.insert(4, "Brain_region", list(Brain_region)*int(PAF_data_df.shape[0]/len(Brain_region)))
# # Save the dataframes
# OOF_data_df.to_pickle(os.path.join(Feature_savepath,"OOF_data_FOOOF_df.pkl"))
"""
"""
# %% Microstate analysis
# The function takes the data as a numpy array (n_t, n_ch)
# The data is already re-referenced to common average
# Variables for the clustering function are extracted
sfreq = final_epochs[0].info["sfreq"]
eye_status = list(final_epochs[0].event_id.keys())
n_eye_status = len(eye_status)
ch_names = final_epochs[0].info["ch_names"]
n_channels = len(ch_names)
locs = np.zeros((n_channels,2)) # xy coordinates of the electrodes
for c in range(n_channels):
locs[c] = final_epochs[0].info["chs"][c]["loc"][0:2]
# The epochs are transformed to numpy arrays
micro_data = []
EC_micro_data = []
EO_micro_data = []
for i in range(n_subjects):
# Transform data to correct shape
micro_data.append(final_epochs[i].get_data()) # get data
arr_shape = micro_data[i].shape # get shape
micro_data[i] = micro_data[i].swapaxes(1,2) # swap ch and time axis
micro_data[i] = micro_data[i].reshape(arr_shape[0]*arr_shape[2],arr_shape[1]) # reshape by combining epochs and times
# Get indices for eyes open and closed
EC_index = final_epochs[i].events[:,2] == 1
EO_index = final_epochs[i].events[:,2] == 2
# Repeat with 4s * sample frequency to correct for concatenation of times and epochs
EC_index = np.repeat(EC_index,4*sfreq)
EO_index = np.repeat(EO_index,4*sfreq)
# Save data where it is divided into eye status
EC_micro_data.append(micro_data[i][EC_index])
EO_micro_data.append(micro_data[i][EO_index])
# Global explained variance and Cross-validation criterion is used to determine number of microstates
# First all data is concatenated to find the optimal number of maps for all data
micro_data_all = np.vstack(micro_data)
# Determine the number of clusters
# I use a slightly modified kmeans function which returns the cv_min
"""
global_gev = []
cv_criterion = []
for n_maps in range(2,7):
maps, L, gfp_peaks, gev, cv_min = kmeans_return_all(micro_data_all, n_maps)
global_gev.append(np.sum(gev))
cv_criterion.append(cv_min)
# Save run results
cluster_results = np.array([global_gev,cv_criterion])
np.save("Microstate_n_cluster_test_results.npy", cluster_results) # (gev/cv_crit, n_maps from 2 to 6)
#cluster_results = np.load("Microstate_n_cluster_test_results.npy")
#global_gev = cluster_results[0,:]
#cv_criterion = cluster_results[1,:]
# Evaluate best n_maps
plt.figure()
plt.plot(np.linspace(2,6,len(cv_criterion)),(cv_criterion/np.sum(cv_criterion)), label="CV Criterion")
plt.plot(np.linspace(2,6,len(cv_criterion)),(global_gev/np.sum(global_gev)), label="GEV")
plt.legend()
plt.ylabel("Normalized to total")
"""
# The lower CV the better.
# But the higher GEV the better.
# Based on the plots and the recommendation by vong Wegner & Laufs 2018
# we used 5 microstates
# In order to compare between groups, I fix the microstates by clustering on data from both groups
# Due to instability of maps when running multiple times, I increased n_maps from 4 to 6
n_maps = 5
mode = ["aahc", "kmeans", "kmedoids", "pca", "ica"][1]
# K-means is stochastic, thus I run it multiple times in order to find the maps with highest GEV
# Each K-means is run 5 times and best map is chosen. But I do this 10 times more, so in total 50 times!
n_run = 10
# Pre-allocate memory
microstate_cluster_results = []
# Parallel processing can only be implemented by ensuring different seeds
# Otherwise the iteration would be the same.
# However the k-means already use parallel processing so making outer loop with
# concurrent processes make it use too many processors
# Get current time
c_time1 = time.localtime()
c_time1 = time.strftime("%a %d %b %Y %H:%M:%S", c_time1)
print(c_time1)
# Change datatype due to error with computational power in clustering
EC_down = np.array(EC_micro_data, dtype = object)
#EC_down = EC_down.astype('float32')
EO_down = np.array(EO_micro_data, dtype = object)
#EO_down = EO_down.astype('float32')
for r in range(n_run):
maps = [0]*2
m_labels = [0]*2
gfp_peaks = [0]*2
gev = [0]*2
# Eyes closed
counter = 0
maps_, x_, gfp_peaks_, gev_ = clustering(
np.vstack(EC_down), sfreq, ch_names, locs, mode, n_maps, doplot=False) # doplot=True is bugged
maps[counter] = maps_
m_labels[counter] = x_
gfp_peaks[counter] = gfp_peaks_
gev[counter] = gev_
counter += 1
# Eyes open
maps_, x_, gfp_peaks_, gev_ = clustering(
np.vstack(EO_down), sfreq, ch_names, locs, mode, n_maps, doplot=False) # doplot=True is bugged
maps[counter] = maps_
m_labels[counter] = x_
gfp_peaks[counter] = gfp_peaks_
gev[counter] = gev_
counter += 1
microstate_cluster_results.append([maps, m_labels, gfp_peaks, gev])
print("Finished {} out of {}".format(r+1, n_run))
# Get current time
c_time2 = time.localtime()
c_time2 = time.strftime("%a %d %b %Y %H:%M:%S", c_time2)
print("Started", c_time1, "\nFinished",c_time2)
# Save the results
with open(Feature_savepath+"Microstate_5_maps_10x5_k_means_results.pkl", "wb") as file:
pickle.dump(microstate_cluster_results, file)
# # Load
# with open(Feature_savepath+"Microstate_4_maps_10x5_k_means_results.pkl", "rb") as file:
# microstate_cluster_results = pickle.load(file)
# Find the best maps (Highest GEV across all the K-means clusters)
EC_total_gevs = np.sum(np.vstack(np.array(microstate_cluster_results)[:,3,0]), axis=1) # (runs, maps/labels/gfp/gev, ec/eo)
EO_total_gevs = np.sum(np.vstack(np.array(microstate_cluster_results)[:,3,1]), axis=1)
Best_EC_idx = np.argmax(EC_total_gevs)
Best_EO_idx = np.argmax(EO_total_gevs)
# Update the variables for the best maps
maps = [microstate_cluster_results[Best_EC_idx][0][0],microstate_cluster_results[Best_EO_idx][0][1]]
m_labels = [microstate_cluster_results[Best_EC_idx][1][0],microstate_cluster_results[Best_EO_idx][1][1]]
gfp_peaks = [microstate_cluster_results[Best_EC_idx][2][0],microstate_cluster_results[Best_EO_idx][2][1]]
gev = [microstate_cluster_results[Best_EC_idx][3][0],microstate_cluster_results[Best_EO_idx][3][1]]
# Plot the maps
plt.style.use('default')
labels = ["EC", "EO"] #Eyes-closed, Eyes-open
for i in range(len(labels)):
fig, axarr = plt.subplots(1, n_maps, figsize=(20,5))
fig.patch.set_facecolor('white')
for imap in range(n_maps):
mne.viz.plot_topomap(maps[i][imap,:], pos = final_epochs[0].info, axes = axarr[imap]) # plot
axarr[imap].set_title("GEV: {:.2f}".format(gev[i][imap]), fontsize=16, fontweight="bold") # title
fig.suptitle("Microstates: {}".format(labels[i]), fontsize=20, fontweight="bold")
# Manual re-order the maps
# Due the random initiation of K-means this have to be modified every time clusters are made!
# Assign map labels (e.g. 0, 2, 1, 3)
order = [0]*2
order[0] = [3,0,1,2,4] # EC
order[1] = [3,1,0,2,4] # EO
for i in range(len(order)):
maps[i] = maps[i][order[i],:] # re-order maps
gev[i] = gev[i][order[i]] # re-order GEV
# Make directory to find and replace map labels
dic0 = {value:key for key, value in enumerate(order[i])}
m_labels[i][:] = [dic0.get(n, n) for n in m_labels[i]] # re-order labels
# The maps seems to be correlated both negatively and positively (see spatial correlation plots)
# Thus the sign of the map does not really reflect which areas are positive or negative (absolute)
# But more which areas are different during each state (relatively)
# I can therefore change the sign of the map for the visualizaiton
sign_swap = [[1,-1,1,1,1],[1,1,1,-1,1]]
for i in range(len(order)):
for m in range(n_maps):
maps[i][m] *= sign_swap[i][m]
# Plot the maps and save
save_path = "/home/s200431/Figures/Microstates"
labels = ["EC", "EO"]
for i in range(len(labels)):
fig, axarr = plt.subplots(1, n_maps, figsize=(20,5))
fig.patch.set_facecolor('white')
for imap in range(n_maps):
mne.viz.plot_topomap(maps[i][imap,:], pos = final_epochs[0].info, axes = axarr[imap]) # plot
axarr[imap].set_title("GEV: {:.2f}".format(gev[i][imap]), fontsize=16, fontweight="bold") # title
fig.suptitle("Microstates: {} - Total GEV: {:.2f}".format(labels[i],sum(gev[i])), fontsize=20, fontweight="bold")
# Save the figure
fig.savefig(os.path.join(save_path,str("Microstates_{}".format(labels[i]) + ".png")))
# Calculate spatial correlation between maps and actual data points (topography)
# The sign of the map is changed so the correlation is positive
# By default the code looks for highest spatial correlation (regardless of sign)
# Thus depending on random initiation point the map might be opposite
plt.style.use('ggplot')
def spatial_correlation(data, maps):
n_t = data.shape[0]
n_ch = data.shape[1]
data = data - data.mean(axis=1, keepdims=True)
# GFP peaks
gfp = np.std(data, axis=1)
gfp_peaks = locmax(gfp)
gfp_values = gfp[gfp_peaks]
gfp2 = np.sum(gfp_values**2) # normalizing constant in GEV
n_gfp = gfp_peaks.shape[0]
# Spatial correlation
C = np.dot(data, maps.T)
C /= (n_ch*np.outer(gfp, np.std(maps, axis=1)))
L = np.argmax(C**2, axis=1) # C is squared here which means the maps do no retain information about the sign of the correlation
return C
C_EC = spatial_correlation(np.vstack(np.array(EC_micro_data)), maps[0])
C_EO = spatial_correlation(np.vstack(np.array(EO_micro_data)), maps[1])
C = [C_EC, C_EO]
# Plot the distribution of spatial correlation for each label and each map
labels = ["EC", "EO"]
for i in range(len(labels)):
fig, axarr = plt.subplots(n_maps, n_maps, figsize=(16,16))
for Lmap in range(n_maps):
for Mmap in range(n_maps):
sns.distplot(C[i][m_labels[i] == Lmap,Mmap], ax = axarr[Lmap,Mmap])
axarr[Lmap,Mmap].set_xlabel("Spatial correlation")
plt.suptitle("Distribution of spatial correlation_{}".format(labels[i]), fontsize=20, fontweight="bold")
# Add common x and y axis labels by making one big axis
fig.add_subplot(111, frameon=False)
plt.tick_params(labelcolor="none", top="off", bottom="off", left="off", right="off") # hide tick labels and ticks
plt.grid(False) # remove global grid
plt.xlabel("Microstate number", labelpad=20)
plt.ylabel("Label number", labelpad=10)
fig.savefig(os.path.join(save_path,str("Microstates_Spatial_Correlation_Label_State_{}".format(labels[i]) + ".png")))
# Plot the distribution of spatial correlation for all data and each map
labels = ["EC", "EO"]
for i in range(len(labels)):
fig, axarr = plt.subplots(1,n_maps, figsize=(20,5))
for imap in range(n_maps):
sns.distplot(C[i][:,imap], ax = axarr[imap])
plt.xlabel("Spatial correlation")
plt.suptitle("Distribution of spatial correlation", fontsize=20, fontweight="bold")
# Add common x and y axis labels by making one big axis
fig.add_subplot(111, frameon=False)
plt.tick_params(labelcolor="none", top="off", bottom="off", left="off", right="off") # hide tick labels and ticks
plt.grid(False) # remove global grid
plt.xlabel("Microstate number", labelpad=20)
plt.ylabel("Label number")
# Prepare for calculation of transition matrix
# I modified the function, so it takes the list argument gap_index
# gap_index should have the indices right before gaps in data
# Gaps: Between dropped epochs, trials (eo/ec) and subjects
# The between subjects gaps is removed by dividing the data into subjects
n_trials = 5
n_epoch_length = final_epochs[0].get_data().shape[2]
micro_labels = []
micro_subject_EC_idx = [0]
micro_subject_EO_idx = [0]
gaps_idx = []
gaps_trials_idx = []
for i in range(n_subjects):
# Get indices for subject
micro_subject_EC_idx.append(micro_subject_EC_idx[i]+EC_micro_data[i].shape[0])
temp_EC = m_labels[0][micro_subject_EC_idx[i]:micro_subject_EC_idx[i+1]]
# Get labels for subject i EO
micro_subject_EO_idx.append(micro_subject_EO_idx[i]+EO_micro_data[i].shape[0])
temp_EO = m_labels[1][micro_subject_EO_idx[i]:micro_subject_EO_idx[i+1]]
# Save
micro_labels.append([temp_EC,temp_EO]) # (subject, eye)
# Get indices with gaps
# Dropped epochs are first considered
# Each epoch last 4s, which correspond to 2000 samples and a trial is 15 epochs - dropped epochs
# Get epochs for each condition
EC_drop_epochs = Drop_epochs_df.iloc[i,1:][Drop_epochs_df.iloc[i,1:] <= 75].to_numpy()
EO_drop_epochs = Drop_epochs_df.iloc[i,1:][(Drop_epochs_df.iloc[i,1:] >= 75)&
(Drop_epochs_df.iloc[i,1:] <= 150)].to_numpy()
# Get indices for the epochs for EC that were dropped and correct for changing index due to drop
EC_drop_epochs_gaps_idx = []
counter = 0
for d in range(len(EC_drop_epochs)):
drop_epoch_number = EC_drop_epochs[d]
Drop_epoch_idx = (drop_epoch_number-counter)*n_epoch_length # counter subtracted as the drop index is before dropped
EC_drop_epochs_gaps_idx.append(Drop_epoch_idx-1) # -1 for point just before gap
counter += 1
# Negative index might occur if the first epochs were removed. This index is not needed for transition matrix
if len(EC_drop_epochs_gaps_idx) > 0:
for d in range(len(EC_drop_epochs_gaps_idx)): # check all, e.g. if epoch 0,1,2,3 are dropped then all should be caught
if EC_drop_epochs_gaps_idx[0] == -1:
EC_drop_epochs_gaps_idx = EC_drop_epochs_gaps_idx[1:len(EC_drop_epochs)]
# Get indices for the epochs for EO that were dropped and correct for changing index due to drop
EO_drop_epochs_gaps_idx = []
counter = 0
for d in range(len(EO_drop_epochs)):
drop_epoch_number = EO_drop_epochs[d]-75
Drop_epoch_idx = (drop_epoch_number-counter)*n_epoch_length # counter subtracted as the drop index is before dropped
EO_drop_epochs_gaps_idx.append(Drop_epoch_idx-1) # -1 for point just before gap
counter += 1
# Negative index might occur if the first epoch was removed. This index is not needed for transition matrix
if len(EO_drop_epochs_gaps_idx) > 0:
for d in range(len(EO_drop_epochs_gaps_idx)): # check all, e.g. if epoch 0,1,2,3 are dropped then all should be caught
if EO_drop_epochs_gaps_idx[0] == -1:
EO_drop_epochs_gaps_idx = EO_drop_epochs_gaps_idx[1:len(EO_drop_epochs)]
# Gaps between trials
Trial_indices = [0, 15, 30, 45, 60, 75] # all the indices for start and end of the 5 trials
EC_trial_gaps_idx = []
EO_trial_gaps_idx = []
counter_EC = 0
counter_EO = 0
for t in range(len(Trial_indices)-2): # -2 as start and end is not used in transition matrix
temp_drop = EC_drop_epochs[(EC_drop_epochs >= Trial_indices[t])&
(EC_drop_epochs < Trial_indices[t+1])]
# Correct the trial id for any potential drops within that trial
counter_EC += len(temp_drop)
trial_idx_corrected_for_drops = 15*(t+1)-counter_EC
EC_trial_gaps_idx.append((trial_idx_corrected_for_drops*n_epoch_length)-1) # multiply id with length of epoch and subtract 1
temp_drop = EO_drop_epochs[(EO_drop_epochs >= Trial_indices[t]+75)&
(EO_drop_epochs < Trial_indices[t+1]+75)]
# Correct the trial id for any potential drops within that trial
counter_EO += len(temp_drop)
trial_idx_corrected_for_drops = 15*(t+1)-counter_EO
EO_trial_gaps_idx.append((trial_idx_corrected_for_drops*n_epoch_length)-1) # multiply id with length of epoch and subtract 1
# Concatenate all drop indices
gaps_idx.append([np.unique(np.sort(EC_drop_epochs_gaps_idx+EC_trial_gaps_idx)),
np.unique(np.sort(EO_drop_epochs_gaps_idx+EO_trial_gaps_idx))])
# Make on with trial gaps only for use in LRTC analysis
gaps_trials_idx.append([EC_trial_gaps_idx,EO_trial_gaps_idx])
# Save the gap idx files
np.save("Gaps_idx.npy",np.array(gaps_idx))
np.save("Gaps_trials_idx.npy",np.array(gaps_trials_idx))
# %% Calculate microstate features
# Symbol distribution (also called ratio of time covered RTT)
# Transition matrix
# Shannon entropy
EC_p_hat = p_empirical(m_labels[0], n_maps)
EO_p_hat = p_empirical(m_labels[1], n_maps)
# Sanity check: Overall between EC and EO
microstate_time_data = np.zeros((n_subjects,n_eye_status,n_maps))
microstate_transition_data = np.zeros((n_subjects,n_eye_status,n_maps,n_maps))
microstate_entropy_data = np.zeros((n_subjects,n_eye_status))
microstate_orrurence_data = np.zeros((n_subjects,n_eye_status,n_maps))
microstate_mean_duration_data = np.zeros((n_subjects,n_eye_status,n_maps))
for i in range(n_subjects):
# Calculate ratio of time covered
temp_EC_p_hat = p_empirical(micro_labels[i][0], n_maps)
temp_EO_p_hat = p_empirical(micro_labels[i][1], n_maps)
# Calcuate number of occurences for each microstate
for j in range(len(micro_labels[i][0])-1):
if micro_labels[i][0][j] != micro_labels[i][0][j+1]:
microstate_orrurence_data[i][0][micro_labels[i][0][j]] += 1
for j in range(len(micro_labels[i][1])-1):
if micro_labels[i][1][j] != micro_labels[i][1][j+1]:
microstate_orrurence_data[i][1][micro_labels[i][1][j]] += 1
# Calculate mean duration of each microstate
for j in range(n_maps):
microstate_mean_duration_data[i][0][j] = sum(micro_labels[i][0] == j)/microstate_orrurence_data[i][0][j]
microstate_mean_duration_data[i][1][j] = sum(micro_labels[i][1] == j)/microstate_orrurence_data[i][1][j]
# Calculate transition matrix
"""
temp_EC_T_hat = T_empirical(micro_labels[i][0], n_maps, gaps_idx[i][0])
temp_EO_T_hat = T_empirical(micro_labels[i][1], n_maps, gaps_idx[i][1])
"""
temp_EC_T_hat = T_empirical(micro_labels[i][0], n_maps)
temp_EO_T_hat = T_empirical(micro_labels[i][1], n_maps)
# Calculate Shannon entropy
temp_EC_h_hat = H_1(micro_labels[i][0], n_maps)
temp_EO_h_hat = H_1(micro_labels[i][1], n_maps)
# Save the data
microstate_time_data[i,0,:] = temp_EC_p_hat
microstate_time_data[i,1,:] = temp_EO_p_hat
microstate_transition_data[i,0,:,:] = temp_EC_T_hat
microstate_transition_data[i,1,:,:] = temp_EO_T_hat
microstate_entropy_data[i,0] = temp_EC_h_hat/max_entropy(n_maps) # ratio of max entropy
microstate_entropy_data[i,1] = temp_EO_h_hat/max_entropy(n_maps) # ratio of max entropy
# Save transition data
np.save(Feature_savepath+"microstate_transition_data.npy", microstate_transition_data)
# Convert transition data to dataframe for further processing with other features
# Transition matrix should be read as probability of row to column
microstate_transition_data_arr =\
microstate_transition_data.reshape((n_subjects,n_eye_status,n_maps*n_maps)) # flatten 5 x 5 matrix to 1D
transition_info = ["M1->M1", "M1->M2", "M1->M3", "M1->M4", "M1->M5",
"M2->M1", "M2->M2", "M2->M3", "M2->M4", "M2-M5",
"M3->M1", "M3->M2", "M3->M3", "M3->M4", "M3->M5",
"M4->M1", "M4->M2", "M4->M3", "M4->M4", "M4->M5",
"M5->M1", "M5->M2", "M5->M3", "M5->M4", "M5->M5"]
arr = np.column_stack(list(map(np.ravel, np.meshgrid(*map(np.arange, microstate_transition_data_arr.shape), indexing="ij"))) + [microstate_transition_data_arr.ravel()])
microstate_transition_data_df = pd.DataFrame(arr, columns = ["Subject_ID", "Eye_status", "Transition", "Value"])
# Change from numerical coding to actual values
eye_status = list(final_epochs[0].event_id.keys())
index_values = [Subject_id,eye_status,transition_info]
for col in range(len(index_values)):
col_name = microstate_transition_data_df.columns[col]
for shape in reversed(range(microstate_transition_data_arr.shape[col])): # notice this is the shape of original numpy array. Not shape of DF
microstate_transition_data_df.loc[microstate_transition_data_df.iloc[:,col] == shape,col_name]\
= index_values[col][shape]
# Add group status
Group_status = np.array(["CTRL"]*len(microstate_transition_data_df["Subject_ID"]))
Group_status[np.array([i in cases for i in microstate_transition_data_df["Subject_ID"]])] = "PTSD"
# Add to dataframe
microstate_transition_data_df.insert(2, "Group_status", Group_status)
# Save df
microstate_transition_data_df.to_pickle(os.path.join(Feature_savepath,"microstate_transition_data_df.pkl"))
# Convert time covered data to Pandas dataframe
# Convert orrurence data to Pandas dataframe
# Convert mean duration data to Pandas dataframe
# The dimensions will each be a column with numbers and the last column will be the actual values
arr = np.column_stack(list(map(np.ravel, np.meshgrid(*map(np.arange, microstate_time_data.shape), indexing="ij"))) + [microstate_time_data.ravel()])
arr_2 = np.column_stack(list(map(np.ravel, np.meshgrid(*map(np.arange, microstate_orrurence_data.shape), indexing="ij"))) + [microstate_orrurence_data.ravel()])
arr_3 = np.column_stack(list(map(np.ravel, np.meshgrid(*map(np.arange, microstate_mean_duration_data.shape), indexing="ij"))) + [microstate_mean_duration_data.ravel()])
microstate_time_df = pd.DataFrame(arr, columns = ["Subject_ID", "Eye_status", "Microstate", "Value"])
microstate_orrurence_df = pd.DataFrame(arr_2, columns = ["Subject_ID", "Eye_status", "Microstate", "Value"])
microstate_mean_duration_df = pd.DataFrame(arr_3, columns = ["Subject_ID", "Eye_status", "Microstate", "Value"])
# Change from numerical coding to actual values
eye_status = list(final_epochs[0].event_id.keys())
microstates = [1,2,3,4,5]
index_values = [Subject_id,eye_status,microstates]
for col in range(len(index_values)):
col_name = microstate_time_df.columns[col]
col_name_2 = microstate_orrurence_df.columns[col]
col_name_3 = microstate_mean_duration_df.columns[col]
for shape in reversed(range(microstate_time_data.shape[col])): # notice this is the shape of original numpy array. Not shape of DF
microstate_time_df.loc[microstate_time_df.iloc[:,col] == shape,col_name]\
= index_values[col][shape]
microstate_orrurence_df.loc[microstate_orrurence_df.iloc[:,col] == shape,col_name_2]\
= index_values[col][shape]
microstate_mean_duration_df.loc[microstate_mean_duration_df.iloc[:,col] == shape,col_name_3]\
= index_values[col][shape]
# Reversed in inner loop is used to avoid sequencial data being overwritten.
# E.g. if 0 is renamed to 1, then the next loop all 1's will be renamed to 2
# Add group status
Group_status = np.array(["CTRL"]*len(microstate_time_df["Subject_ID"]))
Group_status[np.array([i in cases for i in microstate_time_df["Subject_ID"]])] = "PTSD"
Group_status_2 = np.array(["CTRL"]*len(microstate_orrurence_df["Subject_ID"]))
Group_status_2[np.array([i in cases for i in microstate_orrurence_df["Subject_ID"]])] = "PTSD"
Group_status_3 = np.array(["CTRL"]*len(microstate_mean_duration_df["Subject_ID"]))
Group_status_3[np.array([i in cases for i in microstate_mean_duration_df["Subject_ID"]])] = "PTSD"
# Add to dataframe
microstate_time_df.insert(2, "Group_status", Group_status)
microstate_orrurence_df.insert(2, "Group_status", Group_status_2)
microstate_mean_duration_df.insert(2, "Group_status", Group_status_3)
# Save df
microstate_time_df.to_pickle(os.path.join(Feature_savepath,"microstate_time_df.pkl"))
microstate_orrurence_df.to_pickle(os.path.join(Feature_savepath,"microstate_orrurence_df.pkl"))
microstate_mean_duration_df.to_pickle(os.path.join(Feature_savepath,"microstate_mean_duration_df.pkl"))
# Transition data - mean
# Get index for groups
PTSD_idx = np.array([i in cases for i in Subject_id])
CTRL_idx = np.array([not i in cases for i in Subject_id])
n_groups = 2
microstate_transition_data_mean = np.zeros((n_groups,n_eye_status,n_maps,n_maps))
microstate_transition_data_mean[0,:,:,:] = np.mean(microstate_transition_data[PTSD_idx,:,:,:], axis=0)
microstate_transition_data_mean[1,:,:,:] = np.mean(microstate_transition_data[CTRL_idx,:,:,:], axis=0)
# Convert entropy data to Pandas dataframe
# The dimensions will each be a column with numbers and the last column will be the actual values
arr = np.column_stack(list(map(np.ravel, np.meshgrid(*map(np.arange, microstate_entropy_data.shape), indexing="ij"))) + [microstate_entropy_data.ravel()])
microstate_entropy_df = pd.DataFrame(arr, columns = ["Subject_ID", "Eye_status", "Value"])
# Change from numerical coding to actual values
eye_status = list(final_epochs[0].event_id.keys())
index_values = [Subject_id,eye_status]
for col in range(len(index_values)):
col_name = microstate_entropy_df.columns[col]
for shape in reversed(range(microstate_entropy_data.shape[col])): # notice this is the shape of original numpy array. Not shape of DF
microstate_entropy_df.loc[microstate_entropy_df.iloc[:,col] == shape,col_name]\
= index_values[col][shape]
# Reversed in inner loop is used to avoid sequencial data being overwritten.
# E.g. if 0 is renamed to 1, then the next loop all 1's will be renamed to 2
# Add group status
Group_status = np.array(["CTRL"]*len(microstate_entropy_df["Subject_ID"]))
Group_status[np.array([i in cases for i in microstate_entropy_df["Subject_ID"]])] = "PTSD"
# Add to dataframe
microstate_entropy_df.insert(2, "Group_status", Group_status)
# Add dummy variable for re-using plot code
dummy_variable = ["Entropy"]*len(Group_status)
microstate_entropy_df.insert(3, "Measurement", dummy_variable)
# Save df
microstate_entropy_df.to_pickle(os.path.join(Feature_savepath,"microstate_entropy_df.pkl"))
# # %% Long-range temporal correlations (LRTC)
# """
# See Hardstone et al, 2012
# Hurst exponent estimation steps:
# 1. Preprocess
# 2. Band-pass filter for frequency band of interest
# 3. Hilbert transform to obtain amplitude envelope
# 4. Perform DFA
# 4.1 Compute cumulative sum of time series to create signal profile
# 4.2 Define set of window sizes (see below)
# 4.3 Remove the linear trend using least-squares for each window
# 4.4 Calculate standard deviation for each window and take the mean
# 4.5 Plot fluctuation function (Standard deviation) as function
# for all window sizes, on double logarithmic scale
# 4.6 The DFA exponent alpha correspond to Hurst exponent
# f(L) = sd = L^alpha (with alpha as linear coefficient in log plot)
# If 0 < alpha < 0.5: The process exhibits anti-correlations
# If 0.5 < alpha < 1: The process exhibits positive correlations
# If alpha = 0.5: The process is indistinguishable from a random process
# If 1.0 < alpha < 2.0: The process is non-stationary. H = alpha - 1
# Window sizes should be equally spaced on a logarithmic scale
# Sizes should be at least 4 samples and up to 10% of total signal length
# Filters can influence neighboring samples, thus filters should be tested
# on white noise to estimate window sizes that are unaffected by filters
# filter_length=str(2*1/fmin)+"s" # cannot be used with default transition bandwidth
# """
# # From simulations with white noise I determined window size thresholds for the 5 frequency bands:
# thresholds = [7,7,7,6.5,6.5]
# # And their corresponding log step sizes
# with open("LRTC_log_win_sizes.pkl", "rb") as filehandle:
# log_win_sizes = pickle.load(filehandle)
# # Variables for the the different conditions
# # Sampling frequency
# sfreq = final_epochs[0].info["sfreq"]
# # Channels
# ch_names = final_epochs[0].info["ch_names"]
# n_channels = len(ch_names)
# # Frequency
# Freq_Bands = {"delta": [1.25, 4.0],
# "theta": [4.0, 8.0],
# "alpha": [8.0, 13.0],
# "beta": [13.0, 30.0],
# "gamma": [30.0, 49.0]}
# n_freq_bands = len(Freq_Bands)
# # Eye status
# eye_status = list(final_epochs[0].event_id.keys())
# n_eye_status = len(eye_status)
# ### Estimating Hurst exponent for the data
# # The data should be re-referenced to common average (Already done)
# # Data are transformed to numpy arrays
# # Then divided into EO and EC and further into each of the 5 trials
# # So DFA is estimated for each trial separately, which was concluded from simulations
# gaps_trials_idx = np.load("Gaps_trials_idx.npy") # re-used from microstate analysis
# n_trials = 5
# H_data = []
# for i in range(n_subjects):
# # Transform data to correct shape
# temp_arr = final_epochs[i].get_data() # get data
# arr_shape = temp_arr.shape # get shape
# temp_arr = temp_arr.swapaxes(1,2) # swap ch and time axis
# temp_arr = temp_arr.reshape(arr_shape[0]*arr_shape[2],arr_shape[1]) # reshape by combining epochs and times
# # Get indices for eyes open and closed
# EC_index = final_epochs[i].events[:,2] == 1
# EO_index = final_epochs[i].events[:,2] == 2
# # Repeat with 4s * sample frequency to correct for concatenation of times and epochs
# EC_index = np.repeat(EC_index,4*sfreq)
# EO_index = np.repeat(EO_index,4*sfreq)
# # Divide into eye status
# EC_data = temp_arr[EC_index]
# EO_data = temp_arr[EO_index]
# # Divide into trials
# EC_gap_idx = np.array([0]+list(gaps_trials_idx[i,0])+[len(EC_data)])
# EO_gap_idx = np.array([0]+list(gaps_trials_idx[i,1])+[len(EO_data)])
# EC_trial_data = []
# EO_trial_data = []
# for t in range(n_trials):
# EC_trial_data.append(EC_data[EC_gap_idx[t]:EC_gap_idx[t+1]])
# EO_trial_data.append(EO_data[EO_gap_idx[t]:EO_gap_idx[t+1]])
# # Save data
# H_data.append([EC_trial_data,EO_trial_data]) # output [subject][eye][trial][time,ch]
# # Calculate H for each subject, eye status, trial, freq and channel
# H_arr = np.zeros((n_subjects,n_eye_status,n_trials,n_channels,n_freq_bands))
# w_len = [len(ele) for ele in log_win_sizes]
# DFA_arr = np.empty((n_subjects,n_eye_status,n_trials,n_channels,n_freq_bands,2,np.max(w_len)))
# DFA_arr[:] = np.nan
# # Get current time
# c_time1 = time.localtime()
# c_time1 = time.strftime("%a %d %b %Y %H:%M:%S", c_time1)
# print("Started",c_time1)
# # Nolds are already using all cores so multiprocessing with make it slower
# # Warning occurs when R2 is estimated during detrending - but R2 is not used
# warnings.simplefilter("ignore")
# for i in range(n_subjects):
# # Pre-allocate memory
# DFA_temp = np.empty((n_eye_status,n_trials,n_channels,n_freq_bands,2,np.max(w_len)))
# DFA_temp[:] = np.nan
# H_temp = np.empty((n_eye_status,n_trials,n_channels,n_freq_bands))
# for e in range(n_eye_status):
# for trial in range(n_trials):
# for c in range(n_channels):
# # Get the data
# signal = H_data[i][e][trial][:,c]
# counter = 0 # prepare counter
# for fmin, fmax in Freq_Bands.values():
# # Filter for each freq band
# signal_filtered = mne.filter.filter_data(signal, sfreq=sfreq, verbose=0,
# l_freq=fmin, h_freq=fmax)
# # Hilbert transform
# analytic_signal = scipy.signal.hilbert(signal_filtered)
# # Get Amplitude envelope
# # np.abs is the same as np.linalg.norm, i.e. the length for complex input which is the amplitude
# ampltude_envelope = np.abs(analytic_signal)
# # Perform DFA using predefined window sizes from simulation
# a, dfa_data = nolds.dfa(ampltude_envelope,
# nvals=np.exp(log_win_sizes[counter]).astype("int"),
# debug_data=True)
# # Save DFA results
# DFA_temp[e,trial,c,counter,:,0:w_len[counter]] = dfa_data[0:2]
# H_temp[e,trial,c,counter] = a
# # Update counter
# counter += 1
# # Print run status
# print("Finished {} out of {}".format(i+1,n_subjects))
# # Save the results
# H_arr[i] = H_temp
# DFA_arr[i] = DFA_temp
# warnings.simplefilter("default")
# # Get current time
# c_time2 = time.localtime()
# c_time2 = time.strftime("%a %d %b %Y %H:%M:%S", c_time2)
# print("Started", c_time1, "\nCurrent Time",c_time2)
# # Save the DFA analysis data
# np.save(Feature_savepath+"DFA_arr.npy", DFA_arr)
# np.save(Feature_savepath+"H_arr.npy", H_arr)
# # Load
# DFA_arr = np.load(Feature_savepath+"DFA_arr.npy")
# H_arr = np.load(Feature_savepath+"H_arr.npy")
# # Average the Hurst Exponent across trials
# H_arr = np.mean(H_arr, axis=2)
# # Convert to Pandas dataframe (Hurst exponent)
# # The dimensions will each be a column with numbers and the last column will be the actual values
# arr = np.column_stack(list(map(np.ravel, np.meshgrid(*map(np.arange, H_arr.shape), indexing="ij"))) + [H_arr.ravel()])
# H_data_df = pd.DataFrame(arr, columns = ["Subject_ID", "Eye_status", "Channel", "Freq_band", "Value"])
# # Change from numerical coding to actual values
# eye_status = list(final_epochs[0].event_id.keys())
# ch_name = final_epochs[0].info["ch_names"]
# index_values = [Subject_id,eye_status,ch_name,list(Freq_Bands.keys())]
# for col in range(len(index_values)):
# col_name = H_data_df.columns[col]
# for shape in range(H_arr.shape[col]): # notice this is the shape of original numpy array. Not shape of DF
# H_data_df.loc[H_data_df.iloc[:,col] == shape,col_name]\
# = index_values[col][shape]
# # Add group status
# Group_status = np.array(["CTRL"]*len(H_data_df["Subject_ID"]))
# Group_status[np.array([i in cases for i in H_data_df["Subject_ID"]])] = "PTSD"
# # Add to dataframe
# H_data_df.insert(2, "Group_status", Group_status)
# # Fix Freq_band categorical order
# H_data_df["Freq_band"] = H_data_df["Freq_band"].astype("category").\
# cat.reorder_categories(list(Freq_Bands.keys()), ordered=True)
# # Global Hurst exponent
# H_data_df_global = H_data_df.groupby(["Subject_ID", "Eye_status", "Freq_band"]).mean().reset_index() # by default pandas mean skip nan
# # Add group status (cannot use group_by as each subject only have 1 group, not both)
# Group_status = np.array(["CTRL"]*len(H_data_df_global["Subject_ID"]))
# Group_status[np.array([i in cases for i in H_data_df_global["Subject_ID"]])] = "PTSD"
# # Add to dataframe
# H_data_df_global.insert(2, "Group_status", Group_status)
# # Add dummy variable for re-using plot code
# dummy_variable = ["Global Hurst Exponent"]*H_data_df_global.shape[0]
# H_data_df_global.insert(3, "Measurement", dummy_variable )
# # Save the data
# H_data_df.to_pickle(os.path.join(Feature_savepath,"H_data_df.pkl"))
# H_data_df_global.to_pickle(os.path.join(Feature_savepath,"H_data_global_df.pkl"))
# # %% Source localization of sensor data
# # Using non-interpolated channels
# # Even interpolated channels during preprocessing and visual inspection
# # are dropped
# # Prepare epochs for estimation of source connectivity
# source_epochs = [0]*n_subjects
# for i in range(n_subjects):
# source_epochs[i] = final_epochs[i].copy()
# ### Make forward solutions
# # A forward solution is first made for all individuals with no dropped channels
# # Afterwards individual forward solutions are made for subjects with bad
# # channels that were interpolated in preprocessing and these are dropped
# # First forward operator is computed using a template MRI for each dataset
# fs_dir = "/home/glia/MNE-fsaverage-data/fsaverage"
# subjects_dir = os.path.dirname(fs_dir)
# trans = "fsaverage"
# src = os.path.join(fs_dir, "bem", "fsaverage-ico-5-src.fif")
# bem = os.path.join(fs_dir, "bem", "fsaverage-5120-5120-5120-bem-sol.fif")
# # Read the template sourcespace
# sourcespace = mne.read_source_spaces(src)
# temp_idx = 0 # Index with subject that had no bad channels
# subject_eeg = source_epochs[temp_idx].copy()
# subject_eeg.set_eeg_reference(projection=True) # needed for inverse modelling
# # Make forward solution
# fwd = mne.make_forward_solution(subject_eeg.info, trans=trans, src=src,
# bem=bem, eeg=True, mindist=5.0, n_jobs=1)
# # Save forward operator
# fname_fwd = "./Source_fwd/fsaverage-fwd.fif"
# mne.write_forward_solution(fname_fwd, fwd, overwrite=True)
# # A specific forward solution is also made for each subject with bad channels
# with open("./Preprocessing/bad_ch.pkl", "rb") as file:
# bad_ch = pickle.load(file)
# All_bad_ch = bad_ch
# All_drop_epochs = dropped_epochs_df
# All_dropped_ch = []
# Bad_ch_idx = [idx for idx, item in enumerate(All_bad_ch) if item != 0]
# Bad_ch_subjects = All_drop_epochs["Subject_ID"][Bad_ch_idx]
# # For each subject with bad channels, drop the channels and make forward operator
# for n in range(len(Bad_ch_subjects)):
# Subject = Bad_ch_subjects.iloc[n]
# try:
# Subject_idx = Subject_id.index(Subject)
# # Get unique bad channels
# Bad_ch0 = All_bad_ch[Bad_ch_idx[n]]
# Bad_ch1 = []
# for i2 in range(len(Bad_ch0)):
# if type(Bad_ch0[i2]) == list:
# for i3 in range(len(Bad_ch0[i2])):
# Bad_ch1.append(Bad_ch0[i2][i3])
# elif type(Bad_ch0[i2]) == str:
# Bad_ch1.append(Bad_ch0[i2])
# Bad_ch1 = np.unique(Bad_ch1)
# # Drop the bad channels
# source_epochs[Subject_idx].drop_channels(Bad_ch1)
# # Save the overview of dropped channels
# All_dropped_ch.append([Subject,Subject_idx,Bad_ch1])
# # Make forward operator
# subject_eeg = source_epochs[Subject_idx].copy()
# subject_eeg.set_eeg_reference(projection=True) # needed for inverse modelling
# # Make forward solution
# fwd = mne.make_forward_solution(subject_eeg.info, trans=trans, src=src,
# bem=bem, eeg=True, mindist=5.0, n_jobs=1)
# # Save forward operator
# fname_fwd = "./Source_fwd/fsaverage_{}-fwd.fif".format(Subject)
# mne.write_forward_solution(fname_fwd, fwd, overwrite=True)
# except:
# print(Subject,"was already dropped")
# with open("./Preprocessing/All_datasets_bad_ch.pkl", "wb") as filehandle:
# pickle.dump(All_dropped_ch, filehandle)
# # %% Load forward operators
# # Re-use for all subjects without dropped channels
# fname_fwd = "./Source_fwd/fsaverage-fwd.fif"
# fwd = mne.read_forward_solution(fname_fwd)
# fwd_list = [fwd]*n_subjects
# # Use specific forward solutions for subjects with dropped channels
# with open("./Preprocessing/All_datasets_bad_ch.pkl", "rb") as file:
# All_dropped_ch = pickle.load(file)
# for i in range(len(All_dropped_ch)):
# Subject = All_dropped_ch[i][0]
# Subject_idx = All_dropped_ch[i][1]
# fname_fwd = "./Source_fwd/fsaverage_{}-fwd.fif".format(Subject)
# fwd = mne.read_forward_solution(fname_fwd)
# fwd_list[Subject_idx] = fwd
# # Check the correct number of channels are present in fwd
# random_point = int(np.random.randint(0,len(All_dropped_ch)-1,1))
# assert len(fwds[All_dropped_ch[random_point][1]].ch_names) == source_epochs[All_dropped_ch[random_point][1]].info["nchan"]
# # %% Make parcellation
# # After mapping to source space, I end up with 20484 vertices
# # but I wanted to map to fewer sources and not many more
# # Thus I need to perform parcellation
# # Get labels for FreeSurfer "aparc" cortical parcellation (example with 74 labels/hemi - Destriuex)
# labels_aparc = mne.read_labels_from_annot("fsaverage", parc="aparc.a2009s",
# subjects_dir=subjects_dir)
# labels_aparc = labels_aparc[:-2] # remove unknowns
# labels_aparc_names = [label.name for label in labels_aparc]
# # Manually adding the 31 ROIs (14-lh/rh + 3 in midline) from Toll et al, 2020
# # Making fuction to take subset of a label
# def label_subset(label, subset, name="ROI_name"):
# label_subset = mne.Label(label.vertices[subset], label.pos[subset,:],
# label.values[subset], label.hemi,
# name = "{}-{}".format(name,label.hemi),
# subject = label.subject, color = None)
# return label_subset
# ### Visual area 1 (V1 and somatosensory cortex BA1-3)
# label_filenames = ["lh.V1.label", "rh.V1.label",
# "lh.BA1.label", "rh.BA1.label",
# "lh.BA2.label", "rh.BA2.label",
# "lh.BA3a.label", "rh.BA3a.label",
# "lh.BA3b.label", "rh.BA3b.label"]
# labels0 = [0]*len(label_filenames)
# for i, filename in enumerate(label_filenames):
# labels0[i] = mne.read_label(os.path.join(fs_dir, "label", filename), subject="fsaverage")
# # Add V1 to final label variable
# labels = labels0[:2]
# # Rename to remove redundant hemi information
# labels[0].name = "V1-{}".format(labels[0].hemi)
# labels[1].name = "V1-{}".format(labels[1].hemi)
# # Assign a color
# labels[0].color = matplotlib.colors.to_rgba("salmon")
# labels[1].color = matplotlib.colors.to_rgba("salmon")
# # Combine Brodmann Areas for SMC. Only use vertices ones to avoid duplication error
# SMC_labels = labels0[2:]
# for hem in range(2):
# SMC_p1 = SMC_labels[hem]
# for i in range(1,len(SMC_labels)//2):
# SMC_p2 = SMC_labels[hem+2*i]
# p2_idx = np.isin(SMC_p2.vertices, SMC_p1.vertices, invert=True)
# SMC_p21 = label_subset(SMC_p2, p2_idx, "SMC")
# SMC_p1 = SMC_p1.__add__(SMC_p21)
# SMC_p1.name = SMC_p21.name
# # Assign a color
# SMC_p1.color = matplotlib.colors.to_rgba("orange")
# labels.append(SMC_p1)
# ### Inferior frontal junction
# # Located at junction between inferior frontal and inferior precentral sulcus
# label_aparc_names0 = ["S_front_inf","S_precentral-inf-part"]
# temp_labels = []
# for i in range(len(label_aparc_names0)):
# labels_aparc_idx = [labels_aparc_names.index(l) for l in labels_aparc_names if l.startswith(label_aparc_names0[i])]
# for i2 in range(len(labels_aparc_idx)):
# temp_labels.append(labels_aparc[labels_aparc_idx[i2]].copy())
# pos1 = temp_labels[0].pos
# pos2 = temp_labels[2].pos
# distm = scipy.spatial.distance.cdist(pos1,pos2)
# # Find the closest points between the 2 ROIs
# l1_idx = np.unique(np.where(distm<np.quantile(distm, 0.001))[0]) # q chosen to correspond to around 10% of ROI
# l2_idx = np.unique(np.where(distm<np.quantile(distm, 0.0005))[1]) # q chosen to correspond to around 10% of ROI
# IFJ_label_p1 = label_subset(temp_labels[0], l1_idx, "IFJ")
# IFJ_label_p2 = label_subset(temp_labels[2], l2_idx, "IFJ")
# # Combine the 2 parts
# IFJ_label = IFJ_label_p1.__add__(IFJ_label_p2)
# IFJ_label.name = IFJ_label_p1.name
# # Assign a color
# IFJ_label.color = matplotlib.colors.to_rgba("chartreuse")
# # Append to final list
# labels.append(IFJ_label)
# # Do the same for the right hemisphere
# pos1 = temp_labels[1].pos
# pos2 = temp_labels[3].pos
# distm = scipy.spatial.distance.cdist(pos1,pos2)
# # Find the closest points between the 2 ROIs
# l1_idx = np.unique(np.where(distm<np.quantile(distm, 0.00075))[0]) # q chosen to correspond to around 10% of ROI
# l2_idx = np.unique(np.where(distm<np.quantile(distm, 0.0005))[1]) # q chosen to correspond to around 10% of ROI
# IFJ_label_p1 = label_subset(temp_labels[1], l1_idx, "IFJ")
# IFJ_label_p2 = label_subset(temp_labels[3], l2_idx, "IFJ")
# # Combine the 2 parts
# IFJ_label = IFJ_label_p1.__add__(IFJ_label_p2)
# IFJ_label.name = IFJ_label_p1.name
# # Assign a color
# IFJ_label.color = matplotlib.colors.to_rgba("chartreuse")
# # Append to final list
# labels.append(IFJ_label)
# ### Intraparietal sulcus
# label_aparc_names0 = ["S_intrapariet_and_P_trans"]
# labels_aparc_idx = [labels_aparc_names.index(l) for l in labels_aparc_names if l.startswith(label_aparc_names0[0])]
# for i in range(len(labels_aparc_idx)):
# labels.append(labels_aparc[labels_aparc_idx[i]].copy())
# labels[-1].name = "IPS-{}".format(labels[-1].hemi)
# ### Frontal eye field as intersection between middle frontal gyrus and precentral gyrus
# label_aparc_names0 = ["G_front_middle","G_precentral"]
# temp_labels = []
# for i in range(len(label_aparc_names0)):
# labels_aparc_idx = [labels_aparc_names.index(l) for l in labels_aparc_names if l.startswith(label_aparc_names0[i])]
# for i2 in range(len(labels_aparc_idx)):
# temp_labels.append(labels_aparc[labels_aparc_idx[i2]].copy())
# # Take 10% of middle frontal gyrus closest to precentral gyrus (most posterior)
# temp_label0 = temp_labels[0]
# G_fm_y = temp_label0.pos[:,1]
# thres_G_fm_y = np.sort(G_fm_y)[len(G_fm_y)//10]
# idx_p1 = np.where(G_fm_y<thres_G_fm_y)[0]
# FEF_label_p1 = label_subset(temp_label0, idx_p1, "FEF")
# # Take 10% closest for precentral gyrus (most anterior)
# temp_label0 = temp_labels[2]
# # I cannot only use y (anterior/posterior) but also need to restrict z-position
# G_pre_cen_z = temp_label0.pos[:,2]
# thres_G_pre_cen_z = 0.04 # visually inspected threshold
# G_pre_cen_y = temp_label0.pos[:,1]
# thres_G_pre_cen_y = np.sort(G_pre_cen_y[G_pre_cen_z>thres_G_pre_cen_z])[-len(G_pre_cen_y)//10] # notice - for anterior
# idx_p2 = np.where((G_pre_cen_y>thres_G_pre_cen_y) & (G_pre_cen_z>thres_G_pre_cen_z))[0]
# FEF_label_p2 = label_subset(temp_label0, idx_p2, "FEF")
# # Combine the 2 parts
# FEF_label = FEF_label_p1.__add__(FEF_label_p2)
# FEF_label.name = FEF_label_p1.name
# # Assign a color
# FEF_label.color = matplotlib.colors.to_rgba("aqua")
# # Append to final list
# labels.append(FEF_label)
# # Do the same for the right hemisphere
# temp_label0 = temp_labels[1]
# G_fm_y = temp_label0.pos[:,1]
# thres_G_fm_y = np.sort(G_fm_y)[len(G_fm_y)//10]
# idx_p1 = np.where(G_fm_y<thres_G_fm_y)[0]
# FEF_label_p1 = label_subset(temp_label0, idx_p1, "FEF")
# temp_label0 = temp_labels[3]
# G_pre_cen_z = temp_label0.pos[:,2]
# thres_G_pre_cen_z = 0.04 # visually inspected threshold
# G_pre_cen_y = temp_label0.pos[:,1]
# thres_G_pre_cen_y = np.sort(G_pre_cen_y[G_pre_cen_z>thres_G_pre_cen_z])[-len(G_pre_cen_y)//10] # notice - for anterior
# idx_p2 = np.where((G_pre_cen_y>thres_G_pre_cen_y) & (G_pre_cen_z>thres_G_pre_cen_z))[0]
# FEF_label_p2 = label_subset(temp_label0, idx_p2, "FEF")
# # Combine the 2 parts
# FEF_label = FEF_label_p1.__add__(FEF_label_p2)
# FEF_label.name = FEF_label_p1.name
# # Assign a color
# FEF_label.color = matplotlib.colors.to_rgba("aqua")
# # Append to final list
# labels.append(FEF_label)
# ### Supplementary eye fields
# # Located at caudal end of frontal gyrus and upper part of paracentral sulcus
# label_aparc_names0 = ["G_and_S_paracentral","G_front_sup"]
# temp_labels = []
# for i in range(len(label_aparc_names0)):
# labels_aparc_idx = [labels_aparc_names.index(l) for l in labels_aparc_names if l.startswith(label_aparc_names0[i])]
# for i2 in range(len(labels_aparc_idx)):
# temp_labels.append(labels_aparc[labels_aparc_idx[i2]].copy())
# pos1 = temp_labels[0].pos
# pos2 = temp_labels[2].pos
# distm = scipy.spatial.distance.cdist(pos1,pos2)
# # Find the closest points between the 2 ROIs
# l1_idx = np.unique(np.where(distm<np.quantile(distm, 0.0005))[0]) # q chosen to correspond to around 15% of ROI
# l2_idx = np.unique(np.where(distm<np.quantile(distm, 0.005))[1]) # q chosen to correspond to around 10% of ROI
# # Notice that superior frontal gyrus is around 4 times bigger than paracentral
# len(l1_idx)/pos1.shape[0]
# len(l2_idx)/pos2.shape[0]
# # Only use upper part
# z_threshold = 0.06 # visually inspected
# l1_idx = l1_idx[pos1[l1_idx,2] > z_threshold]
# l2_idx = l2_idx[pos2[l2_idx,2] > z_threshold]
# SEF_label_p1 = label_subset(temp_labels[0], l1_idx, "SEF")
# SEF_label_p2 = label_subset(temp_labels[2], l2_idx, "SEF")
# # Combine the 2 parts
# SEF_label = SEF_label_p1.__add__(SEF_label_p2)
# SEF_label.name = SEF_label_p1.name
# # Assign a color
# SEF_label.color = matplotlib.colors.to_rgba("royalblue")
# # Append to final list
# labels.append(SEF_label)
# # Do the same for the right hemisphere
# pos1 = temp_labels[1].pos
# pos2 = temp_labels[3].pos
# distm = scipy.spatial.distance.cdist(pos1,pos2)
# # Find the closest points between the 2 ROIs
# l1_idx = np.unique(np.where(distm<np.quantile(distm, 0.0005))[0]) # q chosen to correspond to around 15% of ROI
# l2_idx = np.unique(np.where(distm<np.quantile(distm, 0.005))[1]) # q chosen to correspond to around 10% of ROI
# # Notice that superior frontal gyrus is around 4 times bigger than paracentral
# len(l1_idx)/pos1.shape[0]
# len(l2_idx)/pos2.shape[0]
# # Only use upper part
# z_threshold = 0.06 # visually inspected
# l1_idx = l1_idx[pos1[l1_idx,2] > z_threshold]
# l2_idx = l2_idx[pos2[l2_idx,2] > z_threshold]
# SEF_label_p1 = label_subset(temp_labels[1], l1_idx, "SEF")
# SEF_label_p2 = label_subset(temp_labels[3], l2_idx, "SEF")
# # Combine the 2 parts
# SEF_label = SEF_label_p1.__add__(SEF_label_p2)
# SEF_label.name = SEF_label_p1.name
# # Assign a color
# SEF_label.color = matplotlib.colors.to_rgba("royalblue")
# # Append to final list
# labels.append(SEF_label)
# ### Posterior cingulate cortex
# label_aparc_names0 = ["G_cingul-Post-dorsal", "G_cingul-Post-ventral"]
# temp_labels = []
# for i in range(len(label_aparc_names0)):
# labels_aparc_idx = [labels_aparc_names.index(l) for l in labels_aparc_names if l.startswith(label_aparc_names0[i])]
# for i2 in range(len(labels_aparc_idx)):
# temp_labels.append(labels_aparc[labels_aparc_idx[i2]].copy())
# labels0 = []
# for hem in range(2):
# PCC_p1 = temp_labels[hem]
# for i in range(1,len(temp_labels)//2):
# PCC_p2 = temp_labels[hem+2*i]
# PCC_p1 = PCC_p1.__add__(PCC_p2)
# PCC_p1.name = "PCC-{}".format(PCC_p1.hemi)
# labels0.append(PCC_p1)
# # Combine the 2 hemisphere in 1 label
# labels.append(labels0[0].__add__(labels0[1]))
# ### Medial prefrontal cortex
# # From their schematic it looks like rostral 1/4 of superior frontal gyrus
# label_aparc_names0 = ["G_front_sup"]
# temp_labels = []
# for i in range(len(label_aparc_names0)):
# labels_aparc_idx = [labels_aparc_names.index(l) for l in labels_aparc_names if l.startswith(label_aparc_names0[i])]
# for i2 in range(len(labels_aparc_idx)):
# temp_labels0 = labels_aparc[labels_aparc_idx[i2]].copy()
# temp_labels0 = temp_labels0.split(4, subjects_dir=subjects_dir)[3]
# temp_labels0.name = "mPFC-{}".format(temp_labels0.hemi)
# temp_labels.append(temp_labels0)
# # Combine the 2 hemisphere in 1 label
# labels.append(temp_labels[0].__add__(temp_labels[1]))
# ### Angular gyrus
# label_aparc_names0 = ["G_pariet_inf-Angular"]
# for i in range(len(label_aparc_names0)):
# labels_aparc_idx = [labels_aparc_names.index(l) for l in labels_aparc_names if l.startswith(label_aparc_names0[i])]
# for i2 in range(len(labels_aparc_idx)):
# temp_labels = labels_aparc[labels_aparc_idx[i2]].copy()
# temp_labels.name = "ANG-{}".format(temp_labels.hemi)
# labels.append(temp_labels)
# ### Posterior middle frontal gyrus
# label_aparc_names0 = ["G_front_middle"]
# for i in range(len(label_aparc_names0)):
# labels_aparc_idx = [labels_aparc_names.index(l) for l in labels_aparc_names if l.startswith(label_aparc_names0[i])]
# for i2 in range(len(labels_aparc_idx)):
# temp_labels = labels_aparc[labels_aparc_idx[i2]].copy()
# temp_labels = temp_labels.split(2, subjects_dir=subjects_dir)[0]
# temp_labels.name = "PMFG-{}".format(temp_labels.hemi)
# labels.append(temp_labels)
# ### Inferior parietal lobule
# # From their parcellation figure seems to be rostral angular gyrus and posterior supramarginal gyrus
# label_aparc_names0 = ["G_pariet_inf-Angular","G_pariet_inf-Supramar"]
# temp_labels = []
# for i in range(len(label_aparc_names0)):
# labels_aparc_idx = [labels_aparc_names.index(l) for l in labels_aparc_names if l.startswith(label_aparc_names0[i])]
# for i2 in range(len(labels_aparc_idx)):
# temp_labels.append(labels_aparc[labels_aparc_idx[i2]].copy())
# # Split angular in 2 and get rostral part
# temp_labels[0] = temp_labels[0].split(2, subjects_dir=subjects_dir)[1]
# temp_labels[1] = temp_labels[1].split(2, subjects_dir=subjects_dir)[1]
# # Split supramarginal in 2 and get posterior part
# temp_labels[2] = temp_labels[2].split(2, subjects_dir=subjects_dir)[0]
# temp_labels[3] = temp_labels[3].split(2, subjects_dir=subjects_dir)[0]
# for hem in range(2):
# PCC_p1 = temp_labels[hem]
# for i in range(1,len(temp_labels)//2):
# PCC_p2 = temp_labels[hem+2*i]
# PCC_p1 = PCC_p1.__add__(PCC_p2)
# PCC_p1.name = "IPL-{}".format(PCC_p1.hemi)
# labels.append(PCC_p1)
# ### Orbital gyrus
# # From their figure it seems to correspond to orbital part of inferior frontal gyrus
# label_aparc_names0 = ["G_front_inf-Orbital"]
# for i in range(len(label_aparc_names0)):
# labels_aparc_idx = [labels_aparc_names.index(l) for l in labels_aparc_names if l.startswith(label_aparc_names0[i])]
# for i2 in range(len(labels_aparc_idx)):
# temp_labels = labels_aparc[labels_aparc_idx[i2]].copy()
# temp_labels.name = "ORB-{}".format(temp_labels.hemi)
# labels.append(temp_labels)
# ### Middle temporal gyrus
# # From their figure it seems to only be 1/4 of MTG at the 2nd to last caudal part
# label_aparc_names0 = ["G_temporal_middle"]
# for i in range(len(label_aparc_names0)):
# labels_aparc_idx = [labels_aparc_names.index(l) for l in labels_aparc_names if l.startswith(label_aparc_names0[i])]
# for i2 in range(len(labels_aparc_idx)):
# temp_labels = labels_aparc[labels_aparc_idx[i2]].copy()
# temp_labels = temp_labels.split(4, subjects_dir=subjects_dir)[1]
# temp_labels.name = "MTG-{}".format(temp_labels.hemi)
# labels.append(temp_labels)
# ### Anterior middle frontal gyrus
# label_aparc_names0 = ["G_front_middle"]
# for i in range(len(label_aparc_names0)):
# labels_aparc_idx = [labels_aparc_names.index(l) for l in labels_aparc_names if l.startswith(label_aparc_names0[i])]
# for i2 in range(len(labels_aparc_idx)):
# temp_labels = labels_aparc[labels_aparc_idx[i2]].copy()
# temp_labels = temp_labels.split(2, subjects_dir=subjects_dir)[1]
# temp_labels.name = "AMFG-{}".format(temp_labels.hemi)
# labels.append(temp_labels)
# ### Insula
# label_aparc_names0 = ["G_Ins_lg_and_S_cent_ins","G_insular_short"]
# temp_labels = []
# for i in range(len(label_aparc_names0)):
# labels_aparc_idx = [labels_aparc_names.index(l) for l in labels_aparc_names if l.startswith(label_aparc_names0[i])]
# for i2 in range(len(labels_aparc_idx)):
# temp_labels.append(labels_aparc[labels_aparc_idx[i2]].copy())
# for hem in range(2):
# PCC_p1 = temp_labels[hem]
# for i in range(1,len(temp_labels)//2):
# PCC_p2 = temp_labels[hem+2*i]
# PCC_p1 = PCC_p1.__add__(PCC_p2)
# PCC_p1.name = "INS-{}".format(PCC_p1.hemi)
# labels.append(PCC_p1)
# ### (Dorsal) Anterior Cingulate Cortex
# label_aparc_names0 = ["G_and_S_cingul-Ant"]
# temp_labels = []
# for i in range(len(label_aparc_names0)):
# labels_aparc_idx = [labels_aparc_names.index(l) for l in labels_aparc_names if l.startswith(label_aparc_names0[i])]
# for i2 in range(len(labels_aparc_idx)):
# temp_labels.append(labels_aparc[labels_aparc_idx[i2]].copy())
# temp_labels[-1].name = "ACC-{}".format(temp_labels[-1].hemi)
# # Combine the 2 hemisphere in 1 label
# labels.append(temp_labels[0].__add__(temp_labels[1]))
# ### Supramarginal Gyrus
# label_aparc_names0 = ["G_pariet_inf-Supramar"]
# for i in range(len(label_aparc_names0)):
# labels_aparc_idx = [labels_aparc_names.index(l) for l in labels_aparc_names if l.startswith(label_aparc_names0[i])]
# for i2 in range(len(labels_aparc_idx)):
# temp_labels = labels_aparc[labels_aparc_idx[i2]].copy()
# temp_labels.name = "SUP-{}".format(temp_labels.hemi)
# labels.append(temp_labels)
# print("{} ROIs have been defined".format(len(labels)))
# # # Visualize positions
# # fig = plt.figure()
# # ax = fig.add_subplot(111, projection="3d")
# # for i in range(0,3):
# # temp_pos = temp_labels[i].pos
# # ax.scatter(temp_pos[:,0],temp_pos[:,1],temp_pos[:,2], marker="o", alpha=0.1)
# # # Add to plot
# # ax.scatter(labels[-1].pos[:,0],labels[-1].pos[:,1],labels[-1].pos[:,2], marker="o")
# # # Visualize the labels
# # # temp_l = labels_aparc[labels_aparc_idx[0]]
# # temp_l = labels[-2]
# # l_stc = stc[100].in_label(temp_l)
# # l_stc.vertices
# # l_stc.plot(**surfer_kwargs)
# # Save the annotation file
# with open("custom_aparc2009_Li_et_al_2022.pkl", "wb") as file:
# pickle.dump(labels, file)
# # %% Calculate orthogonalized power envelope connectivity in source space
# # In non-interpolated channels
# # Updated 22/1 - 2021 to use delta = 1/81 and assumption
# # about non-correlated and equal variance noise covariance matrix for channels
# # Load
# with open("custom_aparc2009_Li_et_al_2022.pkl", "rb") as file:
# labels = pickle.load(file)
# label_names = [label.name for label in labels]
# # Define function to estimate PEC
# def PEC_estimation(x, freq_bands, sfreq=200):
# """
# This function takes a source timeseries signal x and performs:
# 1. Bandpass filtering
# 2. Hilbert transform to yield analytical signal
# 3. Compute all to all connectivity by iteratively computing for each pair
# a. Orthogonalization
# b. Computing power envelopes by squaring the signals |x|^2
# c. Log-transform to enhance normality
# d. Pearson's correlation between each pair
# e. Fisher's r-to-z transform to enhance normality
# The code has been optimized by inspiration from MNE-Python's function:
# mne.connectivity.enelope_correlation.
# In MNE-python version < 0.22 there was a bug, but after the fix in 0.22
# the mne function is equivalent to my implementation, although they don't
# use epsilon but gives same result with a RuntimeWarning about log(0)
# IMPORTANT NOTE:
# Filtering introduce artifacts for first and last timepoint
# The values are very low, more than 1e-12 less than the others
# If they are not removed, then they will heavily influence Pearson's
# correlation as it is outlier sensitive
# Inputs:
# x - The signal in source space as np.array with shape (ROIs,Timepoints)
# freq_bands - The frequency bands of interest as a dictionary e.g.
# {"alpha": [8.0, 13.0], "beta": [13.0, 30.0]}
# sfreq - The sampling frequency in Hertz
# Output:
# The pairwise connectivity matrix
# """
# n_roi, n_timepoints = x.shape
# n_freq_bands = len(freq_bands)
# epsilon = 1e-100 # small value to prevent log(0) errors
# # Filter the signal in the different freq bands
# PEC_con0 = np.zeros((n_roi,n_roi,n_freq_bands))
# for fname, frange in freq_bands.items():
# fmin, fmax = [float(interval) for interval in frange]
# signal_filtered = mne.filter.filter_data(x, sfreq, fmin, fmax,
# fir_design="firwin", verbose=0)
# # Filtering on finite signals will yield very low values for first
# # and last timepoint, which can create outliers. E.g. 1e-29 compared to 1e-14
# # Outlier sensitive methods, like Pearson's correlation, is therefore
# # heavily affected and this systematic error is removed by removing
# # the first and last timepoint
# signal_filtered = signal_filtered[:,1:-1]
# # Hilbert transform
# analytic_signal = scipy.signal.hilbert(signal_filtered)
# # I will use x and y to keep track of orthogonalization
# x0 = analytic_signal
# # Get power envelope
# x0_mag = np.abs(x0)
# # Get scaled conjugate used for orthogonalization estimation
# x0_conj_scaled = x0.conj()
# x0_conj_scaled /= x0_mag
# # Take square power envelope
# PEx = np.square(x0_mag)
# # Take log transform
# lnPEx = np.log(PEx+epsilon)
# # Remove mean for Pearson correlation calculation
# lnPEx_nomean = lnPEx - np.mean(lnPEx, axis=-1, keepdims=True) # normalize each roi timeseries
# # Get std for Pearson correlation calculation
# lnPEx_std = np.std(lnPEx, axis=-1)
# lnPEx_std[lnPEx_std == 0] = 1 # Prevent std = 0 problems
# # Prepare con matrix
# con0 = np.zeros((n_roi,n_roi))
# for roi_r, y0 in enumerate(x0): # for each y0
# # Calculate orthogonalized signal y with respect to x for all x
# # Using y_ort = imag(y*x_conj/|x|)
# # I checked the formula in temp_v3 and it works as intended
# # I want to orthogonalize element wise for each timepoint
# y0_ort = (y0*x0_conj_scaled).imag
# # Here y0_ort.shape = (n_roi, n_timepoints)
# # So y is current roi and the first axis gives each x it is orthogonalized to
# # Take the abs to get power envelope
# y0_ort = np.abs(y0_ort)
# # Prevent log(0) error when calculating y_ort on y
# y0_ort[roi_r] = 1. # this will be 0 zero after mean subtraction
# # Take square power envelope
# PEy = np.square(y0_ort) # squared power envelope
# # Take log transform
# lnPEy = np.log(PEy+epsilon)
# # Remove mean for pearson correlation calculation
# lnPEy_nomean = lnPEy - np.mean(lnPEy, axis=-1, keepdims=True)
# # Get std for Pearson correlation calculation
# lnPEy_std = np.std(lnPEy, axis=-1)
# lnPEy_std[lnPEy_std == 0] = 1.
# # Pearson correlation is expectation of X_nomean * Y_nomean for each time-series divided with standard deviations
# PEC = np.mean(lnPEx_nomean*lnPEy_nomean, axis=-1)
# PEC /= lnPEx_std
# PEC /= lnPEy_std
# con0[roi_r] = PEC
# # The con0 connectivity matrix should be read as correlation between
# # orthogonalized y (row number) and x (column number)
# # It is not symmetrical, as cor(roi2_ort, roi1) is not cor(roi1_ort, roi2)
# # To make it symmetrical the average of the absolute correlation
# # of the 2 possibilities for each pair are taken
# con0 = np.abs(con0)
# con0 = (con0.T+con0)/2.
# # Fisher's z transform - which is equivalent to arctanh
# con0 = np.arctanh(con0)
# # The diagonal is not 0 as I wanted to avoid numerical errors with log(0)
# # and used a small epsilon value. Thus the diagonal is explicitly set to 0
# # Save to array
# PEC_con0[:,:,list(freq_bands.keys()).index(fname)] = con0
# return PEC_con0
# # Prepare variables
# Freq_Bands = {"delta": [1.25, 4.0],
# "theta": [4.0, 8.0],
# "alpha": [8.0, 13.0],
# "beta": [13.0, 30.0],
# "gamma": [30.0, 49.0]}
# n_freq_bands = len(Freq_Bands)
# n_roi = len(labels)
# # Get current time
# c_time1 = time.localtime()
# c_time1 = time.strftime("%a %d %b %Y %H:%M:%S", c_time1)
# print(c_time1)
# # PEC analysis
# PEC_data_list = [0]*n_subjects
# STCs_list = [0]*n_subjects
# # Using inverse operator as generator interferes with concurrent processes
# # If I run it for multiple subjects I run out of ram
# # Thus concurrent processes are used inside the for loop
# def PEC_analysis(input_args): # iterable epoch number and corresponding ts
# i2, ts = input_args
# # Estimate PEC
# PEC_con0 = PEC_estimation(ts, Freq_Bands, sfreq)
# print("Finished {} out of {} epochs".format(i2+1,n_epochs))
# return i2, PEC_con0, ts
# for i in range(n_subjects):
# n_epochs, n_ch, n_timepoints = source_epochs[i].get_data().shape
# # Use different forward solutions depending on number of channels
# cur_subject_id = Subject_id[i]
# fwd = fwds[i]
# # Using assumption about equal variance and no correlations I make a diagonal matrix
# # Using the default option for 0.2µV std for EEG data
# noise_cov = mne.make_ad_hoc_cov(source_epochs[i].info, None)
# # Make inverse operator
# # Using default depth parameter = 0.8 and free orientation (loose = 1)
# inverse_operator = mne.minimum_norm.make_inverse_operator(source_epochs[i].info,
# fwd, noise_cov,
# loose = 1, depth = 0.8,
# verbose = 0)
# src_inv = inverse_operator["src"]
# # Compute inverse solution and retrieve time series for each label
# # Preallocate memory
# label_ts = np.full((n_epochs,len(labels),n_timepoints),np.nan)
# # Define regularization
# snr = 9 # Zhang et al, 2020 used delta = 1/81, which is inverse SNR and correspond to lambda2
# # A for loop is used for each label due to memory issues when doing all labels at the same time
# for l in range(len(labels)):
# stc = mne.minimum_norm.apply_inverse_epochs(source_epochs[i],inverse_operator,
# lambda2 = 1/(snr**2),
# label = labels[l],
# pick_ori = "vector",
# return_generator=False,
# method = "MNE", verbose = 0)
# # Use PCA to reduce the 3 orthogonal directions to 1 principal direction with max power
# # There can be ambiguity about the orientation, thus the one that
# # is pointing most "normal", i.e. closest 90 degrees to the skull is used
# stc_pca = [0]*len(stc)
# for ep in range(n_epochs):
# stc_pca[ep], pca_dir = stc[ep].project(directions="pca", src=src_inv)
# # Get mean time series for the whole label
# temp_label_ts = mne.extract_label_time_course(stc_pca, labels[l], src_inv, mode="mean_flip",
# return_generator=False, verbose=0)
# # Save to array
# label_ts[:,l,:] = np.squeeze(np.array(temp_label_ts))
# print("Finished estimating STC for {} out of {} ROIs".format(l+1,len(labels)))
# # Free up memory
# del stc
# # Prepare variables
# sfreq=source_epochs[i].info["sfreq"]
# n_epochs = len(source_epochs[i])
# # Estimate the pairwise PEC for each epoch
# PEC_con_subject = np.zeros((n_epochs,n_roi,n_roi,n_freq_bands))
# stcs0 = np.zeros((n_epochs,n_roi,int(sfreq)*4)) # 4s epochs
# # Make list of arguments to pass into PEC_analysis using the helper func
# args = []
# for i2 in range(n_epochs):
# args.append((i2,label_ts[i2]))
# with concurrent.futures.ProcessPoolExecutor(max_workers=16) as executor:
# for i2, PEC_result, stc_result in executor.map(PEC_analysis, args): # Function and arguments
# PEC_con_subject[i2] = PEC_result
# stcs0[i2] = stc_result
# # Save to list
# PEC_data_list[i] = PEC_con_subject # [subject](epoch,ch,ch,freq)
# STCs_list[i] = stcs0 # [subject][epoch,roi,timepoint]
# # Print progress
# print("Finished {} out of {} subjects".format(i+1,n_subjects))
# # Get current time
# c_time2 = time.localtime()
# c_time2 = time.strftime("%a %d %b %Y %H:%M:%S", c_time2)
# print("Started", c_time1, "\nFinished",c_time2)
# with open(Feature_savepath+"PEC_each_epoch_drop_interpol_ch_fix_snr.pkl", "wb") as file:
# pickle.dump(PEC_data_list, file)
# with open(Feature_savepath+"STCs_each_epoch_drop_interpol_ch_fix_snr.pkl", "wb") as file:
# pickle.dump(STCs_list, file)
# # # # Load
# # with open(Feature_savepath+"PEC_each_epoch_drop_interpol_ch_fix_snr.pkl", "rb") as file:
# # PEC_data_list = pickle.load(file)
# # # Load
# # with open(Feature_savepath+"STCs_each_epoch_drop_interpol_ch_fix_snr.pkl", "rb") as file:
# # STCs_list = pickle.load(file)
# # Average over eye status
# eye_status = list(source_epochs[0].event_id.keys())
# n_eye_status = len(eye_status)
# pec_data = np.zeros((n_subjects,n_eye_status,n_roi,n_roi,n_freq_bands))
# for i in range(n_subjects):
# # Get indices for eyes open and closed
# EC_index = source_epochs[i].events[:,2] == 1
# EO_index = source_epochs[i].events[:,2] == 2
# # Average over the indices and save to array
# pec_data[i,0] = np.mean(PEC_data_list[i][EC_index], axis=0)
# pec_data[i,1] = np.mean(PEC_data_list[i][EO_index], axis=0)
# # Only use the lower diagonal as the diagonal should be 0 (or very small due to numerical errors)
# # And it is symmetric
# for f in range(n_freq_bands):
# pec_data[i,0,:,:,f] = np.tril(pec_data[i,0,:,:,f],k=-1)
# pec_data[i,1,:,:,f] = np.tril(pec_data[i,1,:,:,f],k=-1)
# # Also save as dataframe format for feature selection
# # Convert to Pandas dataframe
# # The dimensions will each be a column with numbers and the last column will be the actual values
# arr = np.column_stack(list(map(np.ravel, np.meshgrid(*map(np.arange, pec_data.shape), indexing="ij"))) + [pec_data.ravel()])
# pec_data_df = pd.DataFrame(arr, columns = ["Subject_ID", "Eye_status", "chx", "chy", "Freq_band", "Value"])
# # Change from numerical coding to actual values
# eye_status = list(source_epochs[0].event_id.keys())
# freq_bands_name = list(Freq_Bands.keys())
# label_names = [label.name for label in labels]
# index_values = [Subject_id,eye_status,label_names,label_names,freq_bands_name]
# for col in range(len(index_values)):
# col_name = pec_data_df.columns[col]
# for shape in range(pec_data.shape[col]): # notice not dataframe but the array
# pec_data_df.loc[pec_data_df.iloc[:,col] == shape,col_name]\
# = index_values[col][shape]
# # Add group status
# Group_status = np.array(["CTRL"]*len(pec_data_df["Subject_ID"]))
# Group_status[np.array([i in cases for i in pec_data_df["Subject_ID"]])] = "PTSD"
# # Add to dataframe
# pec_data_df.insert(3, "Group_status", Group_status)
# # Remove all diagonal and upper-matrix entries by removing zeros
# pec_data_df = pec_data_df.iloc[pec_data_df["Value"].to_numpy().nonzero()]
# # Save df
# pec_data_df.to_pickle(os.path.join(Feature_savepath,"pec_data_drop_interpol_ch_df.pkl"))
# # %% Sparse clustering of PEC for subtyping PTSD group
# # They did it for both eye status together, so all data in one matrix
# # Load PEC df
# # pec_data_df = pd.read_pickle(os.path.join(Feature_savepath,"pec_data_df.pkl"))
# pec_data_df = pd.read_pickle(os.path.join(Feature_savepath,"pec_data_drop_interpol_ch_df.pkl"))
# # Convert to wide format
# # Make function to add measurement column for indexing
# def add_measurement_column(df, measurement = "Text"):
# dummy_variable = [measurement]*df.shape[0]
# df.insert(1, "Measurement", dummy_variable)
# return df
# # Make function to convert column tuple to string
# def convertTupleHeader(header):
# header = list(header)
# str = "_".join(header)
# return str
# # Prepare overall dataframe
# PEC_df = pd.DataFrame(Subject_id, columns = ["Subject_ID"])
# # Add PEC
# pec_data_df = add_measurement_column(pec_data_df, "PEC")
# temp_df = pec_data_df.pivot_table(index="Subject_ID",columns=["Measurement",
# "Eye_status", "chx", "chy",
# "Freq_band"], dropna=True,
# values="Value").reset_index(drop=True)
# # check NaN is properly dropped and subject index is correct
# assert pec_data_df.shape[0] == np.prod(temp_df.shape)
# test1 = pec_data_df.iloc[np.random.randint(n_subjects),:]
# assert test1["Value"] ==\
# temp_df[test1[1]][test1[2]][test1[3]][test1[5]][test1[6]][Subject_id.index(test1[0])]
# # Fix column names
# temp_df.columns = [convertTupleHeader(temp_df.columns[i]) for i in range(len(temp_df.columns))]
# PEC_df = pd.concat([PEC_df,temp_df], axis=1)
# # Add group status
# Groups = ["CTRL", "PTSD"]
# Group_status = np.array([0]*PEC_df.shape[0]) # CTRL = 0
# Group_status[np.array([i in cases for i in PEC_df["Subject_ID"]])] = 1 # PTSD = 1
# PEC_df.insert(1, "Group_status", Group_status)
# # Only use PTSD patient group
# PEC_df2 = PEC_df.loc[PEC_df["Group_status"]==1,:]
# Subject_info_cols = ["Subject_ID","Group_status"]
# # Use gridsearch and permutations to estimate gap statistic and use it to
# # determine number of clusters and sparsity s
# # I will use 100 permutations and test 2 to 6 clusters as Zhang 2020
# # Error when trying to determine Gap statistic for 1 cluster? Also in R package
# max_clusters = 6
# n_sparsity_feat = 20
# perm_res = []
# for k in range(1,max_clusters):
# # Cannot permute with 1 cluster
# n_clusters = k+1
# x = np.array(PEC_df2.copy().drop(Subject_info_cols, axis=1))
# perm = pysparcl.cluster.permute_modified(x, k=n_clusters, verbose=True,
# nvals=n_sparsity_feat, nperms=100)
# perm_res.append(perm)
# # Save the results
# with open(Feature_savepath+"PEC_drop_interpol_ch_kmeans_perm.pkl", "wb") as file:
# pickle.dump(perm_res, file)
# # # Load
# # with open(Feature_savepath+"PEC_drop_interpol_ch_kmeans_perm.pkl", "rb") as file:
# # perm_res = pickle.load(file)
# # Convert results to array
# perm_res_arr = np.zeros((len(perm_res)*n_sparsity_feat,4))
# for i in range(len(perm_res)):
# _, gaps, sdgaps, wbounds, _ = perm_res[i].values()
# for i2 in range(n_sparsity_feat):
# perm_res_arr[20*i+i2,0] = i+2 # cluster size
# perm_res_arr[20*i+i2,1] = gaps[i2] # gap statistic
# perm_res_arr[20*i+i2,2] = sdgaps[i2] # gap statistic std
# perm_res_arr[20*i+i2,3] = wbounds[i2] # sparsity feature s
# # For each sparsity s, determine best k using one-standard-error criterion
# # Meaning the cluster and sparsity is chosen for the smallest value of k for a fixed s
# # that fulfill Gap(k) >= Gap(k+1)-std(k+1)
# def one_standard_deviation_search(gaps, std):
# best_gaps = np.argmax(gaps)
# current_gaps = gaps[best_gaps]
# current_std = std[best_gaps]
# current_gaps_idx = best_gaps
# while (gaps[current_gaps_idx-1] >= current_gaps - current_std):
# if current_gaps_idx == 0:
# break
# else:
# current_gaps_idx -= 1
# current_gaps = gaps[current_gaps_idx]
# current_std = std[current_gaps_idx]
# out = current_gaps, current_std, current_gaps_idx
# return out
# best_ks = np.zeros((n_sparsity_feat, 2))
# all_s = np.unique(perm_res_arr[:,3])
# plt.figure(figsize=(12,12))
# for i2 in range(n_sparsity_feat):
# current_s = all_s[i2]
# gaps = perm_res_arr[perm_res_arr[:,3] == current_s,1]
# std = perm_res_arr[perm_res_arr[:,3] == current_s,2]
# _, _, idx = one_standard_deviation_search(gaps, std)
# # Save to array
# best_ks[i2,0] = current_s
# best_ks[i2,1] = perm_res_arr[perm_res_arr[:,3] == current_s,0][idx]
# # Plot gap
# plt.errorbar(perm_res_arr[perm_res_arr[:,3] == current_s,0].astype("int"),
# gaps, yerr=std, capsize=5, label = np.round(current_s,3))
# plt.title("Gap statistic for different fixed s")
# plt.legend(loc=1)
# plt.xlabel("Number of clusters")
# plt.ylabel("Gap statistic")
# best_k = int(scipy.stats.mode(best_ks[:,1])[0])
# # Determine s using fixed k as lowest s within 1 std of max gap statistic
# # According to Witten & Tibshirani, 2010
# best_gaps_idx = np.argmax(perm_res_arr[perm_res_arr[:,0] == best_k,1])
# best_gaps = perm_res_arr[perm_res_arr[:,0] == best_k,1][best_gaps_idx]
# best_gaps_std = perm_res_arr[perm_res_arr[:,0] == best_k,2][best_gaps_idx]
# one_std_crit = perm_res_arr[perm_res_arr[:,0] == best_k,1]>=best_gaps-best_gaps_std
# best_s = np.array([perm_res_arr[perm_res_arr[:,0] == best_k,3][one_std_crit][0]])
# # Perform clustering with k clusters
# x = np.array(PEC_df2.copy().drop(Subject_info_cols, axis=1))
# sparcl = pysparcl.cluster.kmeans(x, k=best_k, wbounds=best_s)[0]
# # Save the results
# with open(Feature_savepath+"PEC_drop_interpol_ch_sparse_kmeans.pkl", "wb") as file:
# pickle.dump(sparcl, file)
# # Get overview of the features chosen and summarize feature type with countplot
# nonzero_idx = sparcl["ws"].nonzero()
# sparcl_features = PEC_df2.copy().drop(Subject_info_cols, axis=1).columns[nonzero_idx]
# # Prepare variables
# Freq_Bands = {"delta": [1.25, 4.0],
# "theta": [4.0, 8.0],
# "alpha": [8.0, 13.0],
# "beta": [13.0, 30.0],
# "gamma": [30.0, 49.0]}
# n_freq_bands = len(Freq_Bands)
# eye_status = list(source_epochs[0].event_id.keys())
# n_eye_status = len(eye_status)
# sparcl_feat = []
# sparcl_feat_counts = []
# for e in range(n_eye_status):
# ee = eye_status[e]
# for f in range(n_freq_bands):
# ff = list(Freq_Bands.keys())[f]
# temp_feat = sparcl_features[sparcl_features.str.contains(("_"+ee))]
# temp_feat = temp_feat[temp_feat.str.contains(("_"+ff))]
# # Save to list
# sparcl_feat.append(temp_feat)
# sparcl_feat_counts.append(["{}_{}".format(ee,ff), len(temp_feat)])
# # Convert the list to dataframe to use in countplot
# sparcl_feat_counts_df = pd.DataFrame(columns=["Eye_status", "Freq_band"])
# for i in range(len(sparcl_feat_counts)):
# # If this feature type does not exist, then skip it
# if sparcl_feat_counts[i][1] == 0:
# continue
# ee, ff = sparcl_feat_counts[i][0].split("_")
# counts = sparcl_feat_counts[i][1]
# temp_df = pd.DataFrame({"Eye_status":np.repeat(ee,counts),
# "Freq_band":np.repeat(ff,counts)})
# sparcl_feat_counts_df = sparcl_feat_counts_df.append(temp_df, ignore_index=True)
# # Fix Freq_band categorical order
# cat_type = pd.CategoricalDtype(categories=list(Freq_Bands.keys()), ordered=True)
# sparcl_feat_counts_df["Freq_band"] = sparcl_feat_counts_df["Freq_band"].astype(cat_type)
# plt.figure(figsize=(8,8))
# g = sns.countplot(y="Freq_band", hue="Eye_status", data=sparcl_feat_counts_df)
# plt.title("PEC Sparse K-means features")
# plt.xlabel("Number of non-zero weights")
# plt.ylabel("Frequency Band")
# # %% Functional connectivity in source space
# # MNE implementation of PLV and wPLI is phase across trials(epochs), e.g. for ERPs
# # I'll use my own manually implemented PLV and wPLI across time and then average across epochs
# # Notice that the new MNE-connectivity library now also takes phase across time
# sfreq = final_epochs[0].info["sfreq"]
# # error when using less than 5 cycles for spectrum estimation
# # 1Hz too low with epoch length of 4, thus I changed the fmin to 1.25 for delta
# Freq_Bands = {"delta": [1.25, 4.0],
# "theta": [4.0, 8.0],
# "alpha": [8.0, 13.0],
# "beta": [13.0, 30.0],
# "gamma": [30.0, 49.0]}
# n_freq_bands = len(Freq_Bands)
# freq_centers = np.array([2.5,6,10.5,21.5,40])
# # Convert to tuples for the mne function
# fmin=tuple([list(Freq_Bands.values())[f][0] for f in range(len(Freq_Bands))])
# fmax=tuple([list(Freq_Bands.values())[f][1] for f in range(len(Freq_Bands))])
# # Make linspace array for morlet waves
# freq_centers = np.arange(fmin[0],fmax[-1]+0.25,0.25)
# # Prepare Morlets
# morlets = mne.time_frequency.tfr.morlet(sfreq,freq_centers,n_cycles=3)
# # Make freqs array for indexing
# freqs0 = [0]*n_freq_bands
# for f in range(n_freq_bands):
# freqs0[f] = freq_centers[(freq_centers>=fmin[f]) & (freq_centers<=fmax[f])]
# # The in-built connectivity function gives an (n_channel, n_channel, freqs output
# # For loop over subject ID and eye status is implemented
# n_subjects = len(Subject_id)
# eye_status = list(final_epochs[0].event_id.keys())
# n_eye_status = len(eye_status)
# ch_names = final_epochs[0].info["ch_names"]
# # Load source labels
# with open("custom_aparc2009_Li_et_al_2022.pkl", "rb") as file:
# labels = pickle.load(file)
# label_names = [label.name for label in labels]
# n_sources = len(label_names)
# # Connectivity methods
# connectivity_methods = ["coh","imcoh","plv","wpli"]
# n_con_methods=len(connectivity_methods)
# # Number of pairwise ch connections
# n_ch_connections = scipy.special.comb(n_sources,2, exact=True, repetition=False)
# # Load source time series
# with open(Feature_savepath+"STCs_each_epoch_drop_interpol_ch_fix_snr.pkl", "rb") as file:
# STCs_list = pickle.load(file)
# # I made my own slightly-optimized PLV & WPLI function
# # Version 2 based on Filter + Hilbert instead of Morlets
# def calculate_PLV_WPLI_across_time(data):
# n_ch, n_time0 = data.shape
# x = data.copy()
# # Filter the signal in the different freq bands
# con_array0 = np.zeros((2,n_ch,n_ch,n_freq_bands))
# # con_array0[con_array0==0] = np.nan
# for fname, frange in Freq_Bands.items():
# fmin, fmax = [float(interval) for interval in frange]
# signal_filtered = mne.filter.filter_data(x, sfreq, fmin, fmax,
# fir_design="firwin", verbose=0)
# # Filtering on finite signals will yield very low values for first
# # and last timepoint, which can create outliers. E.g. 1e-29 compared to 1e-14
# # This systematic error is removed by removing the first and last timepoint
# signal_filtered = signal_filtered[:,1:-1]
# # Hilbert transform to get complex signal
# analytic_signal = scipy.signal.hilbert(signal_filtered)
# # Calculate for the lower diagnonal only as it is symmetric
# for ch_r in range(n_ch):
# for ch_c in range(n_ch):
# if ch_r>ch_c:
# # =========================================================================
# # PLV over time correspond to mean across time of the absolute value of
# # the circular length of the relative phases. So PLV will be 1 if
# # the phases of 2 signals maintain a constant lag
# # In equational form: PLV = 1/N * |sum(e^i(phase1-phase2))|
# # In code: abs(mean(exp(1i*phase_diff)))
# # =========================================================================
# # The real part correspond to the amplitude and the imaginary part can be used to calculate the phase
# phase_diff = np.angle(analytic_signal[ch_r])-np.angle(analytic_signal[ch_c])
# # Convert phase difference to complex part i(phase1-phase2)
# phase_diff_im = 0*phase_diff+1j*phase_diff
# # Take the exponential, then the mean followed by absolute value
# PLV = np.abs(np.mean(np.exp(phase_diff_im)))
# # Save to array
# con_array0[0,ch_r,ch_c,list(Freq_Bands.keys()).index(fname)] = PLV
# # =========================================================================
# # PLI over time correspond to the sign of the sine of relative phase
# # differences. So PLI will be 1 if one signal is always leading or
# # lagging behind the other signal. But it is insensitive to changes in
# # relative phase, as long as it is the same signal that leads.
# # If 2 signals are almost in phase, they might shift between lead/lag
# # due to small fluctuations from noise. This would lead to unstable
# # estimation of "phase" synchronisation.
# # The wPLI tries to correct for this by weighting the PLI with the
# # magnitude of the lag, to attenuate noise sources giving rise to
# # near zero phase lag "synchronization"
# # In equational form: WPLI = |E{|phase_diff|*sign(phase_diff)}| / E{|phase_diff|}
# # =========================================================================
# # Calculate the magnitude of phase differences
# phase_diff_mag = np.abs(np.sin(phase_diff))
# # Calculate the signed phase difference (PLI)
# sign_phase_diff = np.sign(np.sin(phase_diff))
# # Calculate the nominator (abs and average across time)
# WPLI_nominator = np.abs(np.mean(phase_diff_mag*sign_phase_diff))
# # Calculate denominator for normalization
# WPLI_denom = np.mean(phase_diff_mag)
# # Calculate WPLI
# WPLI = WPLI_nominator/WPLI_denom
# # Save to array
# con_array0[1,ch_r,ch_c,list(Freq_Bands.keys()).index(fname)] = WPLI
# return con_array0
# # Pre-allocatate memory
# con_data = np.zeros((n_con_methods,n_subjects,n_eye_status,n_sources,n_sources,n_freq_bands))
# n_epochs_matrix = np.zeros((n_subjects,n_eye_status))
# # Get current time
# c_time = time.localtime()
# c_time = time.strftime("%H:%M:%S", c_time)
# print(c_time)
# def connectivity_estimation(i):
# con_data0 = np.zeros((n_con_methods,n_eye_status,n_sources,n_sources,n_freq_bands))
# con_data0[con_data0==0] = np.nan
# n_epochs_matrix0 = np.zeros((n_eye_status))
# for e in range(n_eye_status):
# ee = eye_status[e]
# eye_idx = final_epochs[i].events[:,2] == e+1 # EC = 1 and EO = 2
# # Get source time series
# temp_STC = STCs_list[i][eye_idx]
# # Calculate the coherence and ImgCoh for the given subject and eye status
# con, freqs, times, n_epochs, n_tapers = spectral_connectivity(
# temp_STC, method=connectivity_methods[0:2],
# mode="multitaper", sfreq=sfreq, fmin=fmin, fmax=fmax,
# faverage=True, verbose=0)
# # Save the results in array
# con_data0[0,e,:,:,:] = con[0] # coherence
# con_data0[1,e,:,:,:] = np.abs(con[1]) # Absolute value of ImgCoh to reflect magnitude of ImgCoh
# # Calculate PLV and wPLI for each epoch and then average
# n_epochs0 = temp_STC.shape[0]
# con_data1 = np.zeros((len(connectivity_methods[2:]),n_epochs0,n_sources,n_sources,n_freq_bands))
# for epoch in range(n_epochs0):
# # First the data is retrieved and epoch axis dropped
# temp_data = temp_STC[epoch,:,:]
# # PLV and WPLI value is calculated across timepoints in each freq band
# PLV_WPLI_con = calculate_PLV_WPLI_across_time(temp_data)
# # Save results
# con_data1[0,epoch,:,:,:] = PLV_WPLI_con[0] # phase locking value
# con_data1[1,epoch,:,:,:] = PLV_WPLI_con[1] # weighted phase lag index
# # Take average across epochs for PLV and wPLI
# con_data2 = np.mean(con_data1,axis=1)
# # Save to final array
# con_data0[2,e,:,:,:] = con_data2[0] # phase locking value
# con_data0[3,e,:,:,:] = con_data2[1] # weighted phase lag index
# n_epochs_matrix0[e] = n_epochs
# print("{} out of {} finished".format(i+1,n_subjects))
# return i, con_data0, n_epochs_matrix0
# with concurrent.futures.ProcessPoolExecutor(max_workers=16) as executor:
# for i, con_result, n_epochs_mat in executor.map(connectivity_estimation, range(n_subjects)): # Function and arguments
# con_data[:,i,:,:,:,:] = con_result
# n_epochs_matrix[i] = n_epochs_mat
# # Get current time
# c_time = time.localtime()
# c_time = time.strftime("%H:%M:%S", c_time)
# print(c_time)
# # Save the results
# np.save(Feature_savepath+"Source_drop_interpol_ch_connectivity_measures_data.npy", con_data) # (con_measure,subject,eye,ch,ch,freq)
# # Also save as dataframe format for feature selection
# # Convert to Pandas dataframe
# # The dimensions will each be a column with numbers and the last column will be the actual values
# arr = np.column_stack(list(map(np.ravel, np.meshgrid(*map(np.arange, con_data.shape), indexing="ij"))) + [con_data.ravel()])
# con_data_df = pd.DataFrame(arr, columns = ["Con_measurement", "Subject_ID", "Eye_status", "chx", "chy", "Freq_band", "Value"])
# # Change from numerical coding to actual values
# eye_status = list(final_epochs[0].event_id.keys())
# freq_bands_name = list(Freq_Bands.keys())
# index_values = [connectivity_methods,Subject_id,eye_status,label_names,label_names,freq_bands_name]
# for col in range(len(index_values)):
# col_name = con_data_df.columns[col]
# for shape in range(con_data.shape[col]): # notice not dataframe but the array
# con_data_df.loc[con_data_df.iloc[:,col] == shape,col_name]\
# = index_values[col][shape]
# # Add group status
# Group_status = np.array(["CTRL"]*len(con_data_df["Subject_ID"]))
# Group_status[np.array([i in cases for i in con_data_df["Subject_ID"]])] = "PTSD"
# # Add to dataframe
# con_data_df.insert(3, "Group_status", Group_status)
# # Remove all diagonal and upper-matrix entries
# con_data_df = con_data_df.iloc[con_data_df["Value"].to_numpy().nonzero()]
# # Save df
# con_data_df.to_pickle(os.path.join(Feature_savepath,"con_data_source_drop_interpol_df.pkl"))
# # %% Estimate Granger's Causality in source space
# # Load source labels
# with open("custom_aparc2009_Li_et_al_2022.pkl", "rb") as file:
# labels = pickle.load(file)
# label_names = [label.name for label in labels]
# n_sources = len(label_names)
# # Load source time series
# with open(Feature_savepath+"STCs_each_epoch_drop_interpol_ch_fix_snr.pkl", "rb") as file:
# STCs_list = pickle.load(file)
# # Granger's causality might be influenced by volume conduction, thus working with CSD might be beneficial
# # But since I already used source modelling to alleviate this problem I will not apply CSD
# # Barrett et al, 2012 also do not apply CSD on source GC
# # GC assumes stationarity, thus I will test for stationarity using ADF test
# # The null hypothesis of ADF is that it has unit root, i.e. is non-stationary
# # I will test how many can reject the null hypothesis, i.e. are stationary
# # Due to the low numerical values in STC the ADF test is unstable, thus I multiply it to be around 1e0
# stationary_test_arr = [0]*n_subjects
# n_tests = [0]*n_subjects
# for i in range(n_subjects):
# # Get data
# data_arr = STCs_list[i]
# # Get shape
# n_epochs, n_channels, n_timepoints = data_arr.shape
# # Create array for indices to print out progress
# ep_progress_idx = np.arange(n_epochs//5,n_epochs,n_epochs//5)
# # Calculate number of tests performed for each subject
# n_tests[i] = n_epochs*n_channels
# # Prepare empty array (with 2's as 0 and 1 will be used)
# stationary_test_arr0 = np.zeros((n_epochs,n_channels))+2 # make array of 2's
# for ep in range(n_epochs):
# for c in range(n_channels):
# ADF = adfuller(data_arr[ep,c,:]*1e14) # multilying with a constant does not change ADF, but helps against numerical instability
# p_value = ADF[1]
# if p_value < 0.05:
# stationary_test_arr0[ep,c] = True # Stationary set to 1
# else:
# stationary_test_arr0[ep,c] = False # Non-stationary set to 0
# # Print partial progress
# if len(np.where(ep_progress_idx==ep)[0]) > 0:
# print("Finished epoch number: {} out of {}".format(ep,n_epochs))
# # Indices that were not tested
# no_test_idx = np.where(stationary_test_arr0==2)[0]
# if len(no_test_idx) > 0:
# print("An unexpected error occurred and {} was not tested".format(no_test_idx))
# # Save to list
# stationary_test_arr[i] = stationary_test_arr0
# # Print progress
# print("Finished subject {} out of {}".format(i+1,n_subjects))
# with open(Stat_savepath+"Source_drop_interpol_GC_stationarity_tests.pkl", "wb") as filehandle:
# # The data is stored as binary data stream
# pickle.dump(stationary_test_arr, filehandle)
# # I used a threshold of 0.05
# # This means that on average I would expect 5% false positives among the tests that showed significance for stationarity
# ratio_stationary = [0]*n_subjects
# for i in range(n_subjects):
# # Ratio of tests that showed stationarity
# ratio_stationary[i] = np.sum(stationary_test_arr[i])/n_tests[i]
# print("Ratio of stationary time series: {0:.3f}".format(np.mean(ratio_stationary))) # 88%
# # The pre-processing have already ensured that most of the data fulfills the stationarity assumption.
# # Divide the data into eyes closed and open
# ch_names = label_names
# n_channels = len(ch_names)
# STC_eye_data = []
# for i in range(n_subjects):
# # Get index for eyes open and eyes closed
# EC_index = final_epochs[i].events[:,2] == 1
# EO_index = final_epochs[i].events[:,2] == 2
# # Get the data
# EC_epoch_data = STCs_list[i][EC_index,:,:] # eye index
# EO_epoch_data = STCs_list[i][EO_index,:,:]
# # Save to list
# STC_eye_data.append([EC_epoch_data, EO_epoch_data])
# # Make each epoch a TimeSeries object
# # Input for TimeSeries is: (ch, time)
# eye_status = list(final_epochs[0].event_id.keys())
# n_eye_status = len(eye_status)
# sfreq = final_epochs[0].info["sfreq"]
# Timeseries_data = []
# for i in range(n_subjects):
# temp_list1 = []
# for e in range(n_eye_status):
# temp_list2 = []
# n_epochs = STC_eye_data[i][e].shape[0]
# for ep in range(n_epochs):
# # Convert to TimeSeries
# time_series = nts.TimeSeries(STC_eye_data[i][e][ep,:,:], sampling_rate=sfreq)
# # Save the object
# temp_list2.append(time_series)
# # Save the timeseries across eye status
# temp_list1.append(temp_list2)
# # Save the timeseries across subjects
# Timeseries_data.append(temp_list1) # output [subject][eye][epoch](ch,time)
# # Test multiple specified model orders of AR models, each combination has its own model
# m_orders = np.linspace(1,25,25) # the model orders tested
# m_orders = np.round(m_orders)
# n_timepoints = len(Timeseries_data[0][0][0])
# n_ch_combinations = scipy.special.comb(n_channels,2, exact=True, repetition=False)
# # To reduce computation time I only test representative epochs (1 from each 1 min session)
# # There will be 5 epochs from eyes closed and 5 from eyes open
# n_rep_epoch = 5
# # The subjects have different number of epochs due to dropped epochs
# gaps_trials_idx = np.load("Gaps_trials_idx.npy") # time_points between sessions
# # I convert the gap time points to epoch number used as representative epoch
# epoch_idx = np.zeros((n_subjects,n_eye_status,n_rep_epoch), dtype=int) # prepare array
# epoch_idx[:,:,0:4] = np.round(gaps_trials_idx/n_timepoints,0)-8 # take random epoch from sessions 1 to 4
# epoch_idx[:,:,4] = np.round(gaps_trials_idx[:,:,3]/n_timepoints,0)+5 # take random epoch from session 5
# # Checking if all epoch idx exists
# for i in range(n_subjects):
# EC_index = final_epochs[i].events[:,2] == 1
# EO_index = final_epochs[i].events[:,2] == 2
# assert np.sum(EC_index) >= epoch_idx[i,0,4]
# assert np.sum(EO_index) >= epoch_idx[i,1,4]
# # Prepare model order estimation
# AIC_arr = np.zeros((len(m_orders),n_subjects,n_eye_status,n_rep_epoch,n_ch_combinations))
# BIC_arr = np.zeros((len(m_orders),n_subjects,n_eye_status,n_rep_epoch,n_ch_combinations))
# def GC_model_order_est(i):
# AIC_arr0 = np.zeros((len(m_orders),n_eye_status,n_rep_epoch,n_ch_combinations))
# BIC_arr0 = np.zeros((len(m_orders),n_eye_status,n_rep_epoch,n_ch_combinations))
# for e in range(n_eye_status):
# n_epochs = STC_eye_data[i][e].shape[0]
# N_total = n_timepoints*n_epochs # total number of datapoints for specific eye condition
# for ep in range(n_rep_epoch):
# epp = epoch_idx[i,e,ep]
# for o in range(len(m_orders)):
# order = int(m_orders[o])
# # Make the Granger Causality object
# GCA1 = nta.GrangerAnalyzer(Timeseries_data[i][e][epp-1], order=order,
# n_freqs=2000)
# for c in range(n_ch_combinations):
# # Retrieve error covariance matrix for all combinations
# ecov = np.array(list(GCA1.error_cov.values()))
# # Calculate AIC
# AIC = ntsu.akaike_information_criterion(ecov[c,:,:], p = n_channels,
# m=order, Ntotal=N_total)
# # Calculate BIC
# BIC = ntsu.bayesian_information_criterion(ecov[c,:,:], p = n_channels,
# m=order, Ntotal=N_total)
# # Save the information criterions
# AIC_arr0[o,e,ep,c] = AIC
# BIC_arr0[o,e,ep,c] = BIC
# print("{} out of {} finished testing".format(i+1,n_subjects))
# return i, AIC_arr0, BIC_arr0
# # Get current time
# c_time1 = time.localtime()
# c_time1 = time.strftime("%a %d %b %Y %H:%M:%S", c_time1)
# print(c_time1)
# with concurrent.futures.ProcessPoolExecutor() as executor:
# for i, AIC_result, BIC_result in executor.map(GC_model_order_est, range(n_subjects)): # Function and arguments
# AIC_arr[:,i] = AIC_result
# BIC_arr[:,i] = BIC_result
# # Get current time
# c_time2 = time.localtime()
# c_time2 = time.strftime("%a %d %b %Y %H:%M:%S", c_time2)
# print("Started", c_time1, "\nCurrent Time",c_time2)
# # Save the AIC and BIC results
# np.save(Feature_savepath+"AIC_Source_drop_interpol_GC_model_order.npy", AIC_arr) # (m. order, subject, eye, epoch, combination)
# np.save(Feature_savepath+"BIC_Source_drop_interpol_GC_model_order.npy", BIC_arr) # (m. order, subject, eye, epoch, combination)
# # Load data
# AIC_arr = np.load(Feature_savepath+"AIC_Source_drop_interpol_GC_model_order.npy")
# BIC_arr = np.load(Feature_savepath+"BIC_Source_drop_interpol_GC_model_order.npy")
# # Average across all subjects, eye status, epochs and combinations
# plt.figure(figsize=(8,6))
# plt.plot(m_orders, np.nanmean(AIC_arr, axis=(1,2,3,4)), label="AIC")
# plt.plot(m_orders, np.nanmean(BIC_arr, axis=(1,2,3,4)), label="BIC")
# plt.title("Average information criteria value")
# plt.xlabel("Model order (Lag)")
# plt.legend()
# np.sum(np.isnan(AIC_arr))/AIC_arr.size # around 0.07% NaN due to non-convergence
# np.sum(np.isnan(BIC_arr))/BIC_arr.size # around 0.07% NaN due to non-convergence
# # If we look at each subject
# mean_subject_AIC = np.nanmean(AIC_arr, axis=(2,3,4))
# plt.figure(figsize=(8,6))
# for i in range(n_subjects):
# plt.plot(m_orders, mean_subject_AIC[:,i])
# plt.title("Average AIC for each subject")
# plt.xlabel("Model order (Lag)")
# mean_subject_BIC = np.nanmean(BIC_arr, axis=(2,3,4))
# plt.figure(figsize=(8,6))
# for i in range(n_subjects):
# plt.plot(m_orders, mean_subject_BIC[:,i])
# plt.title("Average BIC for each subject")
# plt.xlabel("Model order (Lag)")
# # We see that for many cases in BIC, it does not converge. Monotonic increasing!
# # We can look at the distribution of chosen order for each time series analyzed
# # I.e. I will find the minima in model order for each model
# AIC_min_arr = np.argmin(AIC_arr, axis=0)
# BIC_min_arr = np.argmin(BIC_arr, axis=0)
# # Plot the distributions of the model order chosen
# plt.figure(figsize=(8,6))
# sns.distplot(AIC_min_arr.reshape(-1)+1, kde=False, norm_hist=True,
# bins=np.linspace(0.75,30.25,60), label="AIC")
# plt.ylabel("Frequency density")
# plt.xlabel("Model order")
# plt.title("AIC Model Order Estimation")
# plt.figure(figsize=(8,6))
# sns.distplot(BIC_min_arr.reshape(-1)+1, kde=False, norm_hist=True, color="blue",
# bins=np.linspace(0.75,30.25,60), label="BIC")
# plt.ylabel("Frequency density")
# plt.xlabel("Model order")
# plt.title("BIC Model Order Estimation")
# # It is clear from the BIC model that most have model order 1
# # which reflect their monotonic increasing nature without convergence
# # Thus I will only use AIC
# # There is a bias variance trade-off with model order [Stokes & Purdon, 2017]
# # Lower order is associated with higher bias and higher order with variance
# # I will choose the model order that is chosen the most (i.e. majority voting)
# AR_order = int(np.nanquantile(AIC_min_arr.reshape(-1), q=0.5))
# # Order = 5
# # Calculate Granger Causality for each subject, eye and epoch
# Freq_Bands = {"delta": [1.25, 4.0],
# "theta": [4.0, 8.0],
# "alpha": [8.0, 13.0],
# "beta": [13.0, 30.0],
# "gamma": [30.0, 49.0]}
# n_freq_bands = len(Freq_Bands)
# # Pre-allocate memory
# GC_data = np.zeros((2,n_subjects,n_eye_status,n_channels,n_channels,n_freq_bands))
# def GC_analysis(i):
# GC_data0 = np.zeros((2,n_eye_status,n_channels,n_channels,n_freq_bands))
# for e in range(n_eye_status):
# n_epochs = STC_eye_data[i][e].shape[0]
# # Make temporary array to save GC for each epoch
# temp_GC_data = np.zeros((2,n_epochs,n_channels,n_channels,n_freq_bands))
# for ep in range(n_epochs):
# # Fit the AR model
# GCA = nta.GrangerAnalyzer(Timeseries_data[i][e][ep], order=AR_order,
# n_freqs=int(800)) # n_Freq=800 correspond to step of 0.25Hz, the same as multitaper for power estimation
# for f in range(n_freq_bands):
# # Define lower and upper band
# f_lb = list(Freq_Bands.values())[f][0]
# f_ub = list(Freq_Bands.values())[f][1]
# # Get index corresponding to the frequency bands of interest
# freq_idx_G = np.where((GCA.frequencies >= f_lb) * (GCA.frequencies < f_ub))[0]
# # Calculcate Granger causality quantities
# g_xy = np.mean(GCA.causality_xy[:, :, freq_idx_G], -1) # avg on last dimension
# g_yx = np.mean(GCA.causality_yx[:, :, freq_idx_G], -1) # avg on last dimension
# # Transpose to use same format as con_measurement and save
# temp_GC_data[0,ep,:,:,f] = np.transpose(g_xy)
# temp_GC_data[1,ep,:,:,f] = np.transpose(g_yx)
# # Average over epochs for each person, eye condition, direction and frequency band
# temp_GC_epoch_mean = np.nanmean(temp_GC_data, axis=1) # sometimes Log(Sxx/xx_auto_component) is nan
# # Save to array
# GC_data0[:,e,:,:,:] = temp_GC_epoch_mean
# print("{} out of {} finished analyzing".format(i+1,n_subjects))
# return i, GC_data0
# # Get current time
# c_time1 = time.localtime()
# c_time1 = time.strftime("%a %d %b %Y %H:%M:%S", c_time1)
# print(c_time1)
# with concurrent.futures.ProcessPoolExecutor() as executor:
# for i, GC_result in executor.map(GC_analysis, range(n_subjects)): # Function and arguments
# GC_data[:,i] = GC_result
# # Get current time
# c_time2 = time.localtime()
# c_time2 = time.strftime("%a %d %b %Y %H:%M:%S", c_time2)
# print("Started", c_time1, "\nCurrent Time",c_time2)
# # Output: GC_data (g_xy/g_yx, subject, eye, chx, chy, freq)
# # Notice that for g_xy ([0,...]) it means "chy" Granger causes "chx"
# # and for g_yx ([1,...]) it means "chx" Granger causes "chy"
# # This is due to the transposing which flipped the results on to the lower-part of the diagonal
# # Save the Granger_Causality data
# np.save(Feature_savepath+"Source_drop_interpol_GrangerCausality_data.npy", GC_data)
# # Theoretically negative GC values should be impossible, but in practice
# # they can still occur due to problems with model fitting (see Stokes & Purdon, 2017)
# print("{:.3f}% negative GC values".\
# format(np.sum(GC_data[~np.isnan(GC_data)]<0)/np.sum(~np.isnan(GC_data))*100)) # 0.08% negative values
# # These values cannot be interpreted, but seems to occur mostly for true non-causal connections
# # Hence I set them to 0
# with np.errstate(invalid="ignore"): # invalid number refers to np.nan, which will be set to False for comparisons
# GC_data[(GC_data<0)] = 0
# # Save as dataframe for further processing with other features
# # Convert to Pandas dataframe
# # The dimensions will each be a column with numbers and the last column will be the actual values
# arr = np.column_stack(list(map(np.ravel, np.meshgrid(*map(np.arange, GC_data.shape), indexing="ij"))) + [GC_data.ravel()])
# GC_data_df = pd.DataFrame(arr, columns = ["GC_direction", "Subject_ID", "Eye_status", "chx", "chy", "Freq_band", "Value"])
# # Change from numerical coding to actual values
# eye_status = list(final_epochs[0].event_id.keys())
# freq_bands_name = list(Freq_Bands.keys())
# GC_directions_info = ["chy -> chx", "chx -> chy"]
# index_values = [GC_directions_info,Subject_id,eye_status,ch_names,ch_names,freq_bands_name]
# for col in range(len(index_values)):
# col_name = GC_data_df.columns[col]
# for shape in range(GC_data.shape[col]): # notice not dataframe but the array
# GC_data_df.loc[GC_data_df.iloc[:,col] == shape,col_name]\
# = index_values[col][shape]
# # Add group status
# Group_status = np.array(["CTRL"]*len(GC_data_df["Subject_ID"]))
# Group_status[np.array([i in cases for i in GC_data_df["Subject_ID"]])] = "PTSD"
# # Add to dataframe
# GC_data_df.insert(3, "Group_status", Group_status)
# # Remove all nan (including diagonal and upper-matrix entries)
# GC_data_df = GC_data_df.iloc[np.invert(np.isnan(GC_data_df["Value"].to_numpy()))]
# # Swap ch values for GC_direction chy -> chx (so it is always chx -> chy)
# tempchy = GC_data_df[GC_data_df["GC_direction"] == "chy -> chx"]["chy"] # save chy
# GC_data_df.loc[GC_data_df["GC_direction"] == "chy -> chx","chy"] =\
# GC_data_df.loc[GC_data_df["GC_direction"] == "chy -> chx","chx"] # overwrite old chy
# GC_data_df.loc[GC_data_df["GC_direction"] == "chy -> chx","chx"] = tempchy # overwrite chx
# # Drop the GC_direction column
# GC_data_df = GC_data_df.drop("GC_direction", axis=1)
# # Save df
# GC_data_df.to_pickle(os.path.join(Feature_savepath,"GC_data_source_drop_interpol_df.pkl"))
# # Testing if df was formatted correctly
# expected_GC_values = n_subjects*n_eye_status*n_ch_combinations*n_freq_bands*2 # 2 because it is bidirectional
# assert GC_data_df.shape[0] == expected_GC_values
# # Testing a random GC value
# random_connection = np.random.randint(0,GC_data_df.shape[0])
# test_connection = GC_data_df.iloc[random_connection,:]
# i = np.where(Subject_id==test_connection["Subject_ID"])[0]
# e = np.where(np.array(eye_status)==test_connection["Eye_status"])[0]
# chx = np.where(np.array(ch_names)==test_connection["chx"])[0]
# chy = np.where(np.array(ch_names)==test_connection["chy"])[0]
# f = np.where(np.array(freq_bands_name)==test_connection["Freq_band"])[0]
# value = test_connection["Value"]
# if chx < chy: # the GC array is only lower diagonal to save memory
# assert GC_data[0,i,e,chy,chx,f] == value
# if chx > chy:
# assert GC_data[1,i,e,chx,chy,f] == value