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# -*- coding: utf-8 -*-
"""
Functions for the analysis of two-brain microstates
@author: Qianliang Li (glia@dtu.dk)
"""
import os
import numpy as np
import pandas as pd
import mne
import mat73
import matplotlib.pyplot as plt
import nolds
from eeg_microstates3 import (locmax, T_empirical, H_1, H_2)
def load_epoch_from_fieldtrip(i, files, event_id):
"""Loads the preprocessed EEG data """
# Get environmental variables
Subject_id = os.environ.get('Subject_id')
# Load the file
tmp_dict = mat73.loadmat(files[i])
s_id = Subject_id[i]
eeg = tmp_dict["eeg_ppn"+str(s_id)[-1]+"_seg"]
# Get the epoched time series data (epoch, ch, time)
data = np.stack(eeg["trial"])
# Re-scale data
data *= 1e-6 # micro Volt to Volt
# Create info instance
ch_names = [ele[0] for ele in eeg["hdr"]["label"]]
sfreq = int(eeg["fsample"])
eeg_info = mne.create_info(ch_names, sfreq,ch_types="eeg")
# Update filter info
mne.filter._filt_update_info(eeg_info, True, 1.0, 40)
# Make events array (epoch number, _, event id)
events = np.zeros((len(data),3)).astype(int)
events[:,0] = np.arange(0,len(data))
events[:,2] = eeg["trialinfo"][:,0]
# Save trialinfo object for synchronization of pairs based on timings
trialinfo = eeg["trialinfo"][:,0:3]
# Create the Epochs
epoch = mne.EpochsArray(data, eeg_info, events, 0, event_id)
# Set montage
epoch.set_montage("biosemi64")
return epoch, trialinfo
# The function takes the data as a numpy array (n_t, n_ch)
def prepare_1P_micro_arr(i, ppn2_correction, sfreq, freq_range=None, standardize:bool=True):
""" Prepare single-brain EEG data for microstate analysis """
# Get environmental variables
Subject_id = os.environ.get('Subject_id')
# Load epochs
epoch, trialinfo = load_epoch_from_fieldtrip(i)
# Ensure it is averaged referenced
epoch = epoch.set_eeg_reference("average")
# Get numpy arrays
micro_data = epoch.get_data() # get data
if standardize == True:
# Standardize data - will make EEG amplitudes comparable between
micro_data = micro_data - micro_data.mean(axis=1, keepdims=True)
micro_data /= micro_data.std(axis=1, keepdims=True)
# Correct the event_id for ppn2 in each pair, to swap all 6 with 4
# and 7 with 5 and vice versa
if str(Subject_id[i])[-1]=="2":
trialinfo_df = pd.DataFrame(trialinfo.copy())
trialinfo_df.iloc[:,0].replace(ppn2_correction, inplace=True)
trialinfo = trialinfo_df.to_numpy()
# Transform data to correct shape
arr_shape = micro_data.shape # get shape
micro_data = micro_data.swapaxes(1,2) # swap ch and time axis
micro_data = micro_data.reshape(arr_shape[0]*arr_shape[2],arr_shape[1]) # reshape by combining epochs and times
# Filter the data
if freq_range == None:
micro_data_filtered = micro_data # do not perform filtering
elif not freq_range == None:
# Notice it is done only after combining epochs and time, as filter length
# would be too long for 1s epochs. The filter function wants time on the last axis
micro_data = micro_data.transpose()
micro_data_filtered = mne.filter.filter_data(micro_data, sfreq, freq_range[0], freq_range[1])
# Reverse the shape
micro_data_filtered = micro_data_filtered.transpose()
return micro_data_filtered, trialinfo
def plot_microstates(n_maps, maps, gev, epoch_info):
""" Plot the microstates """
fig, axarr = plt.subplots(1, n_maps, figsize=(5*n_maps,5))
fig.patch.set_facecolor('white')
for imap in range(n_maps):
mne.viz.plot_topomap(maps[imap,:], pos = epoch_info, axes = axarr[imap]) # plot
axarr[imap].set_title("GEV: {:.2f}".format(gev[imap]), fontsize=16, fontweight="bold") # title
fig.suptitle("Microstates: {}".format(n_maps), fontsize=20, fontweight="bold")
plt.tight_layout()
return fig
def reorder_microstate_results(new_order, maps, gev, m_labels):
""" Function to re-order microstates based on manual order """
reordered_maps = maps[new_order,:]
reordered_gev = gev[new_order]
# Make directory to find and replace map labels
dic0 = {value:key for key, value in enumerate(new_order)}
reordered_m_labels = np.array([dic0.get(n, n) for n in m_labels]) # re-order labels
return reordered_maps, reordered_gev, reordered_m_labels
def microstate_run_length_encoding(m_labels):
""" Take a 1D stream of microstates and returns the label and duration """
# Adapted from RLE Matlab code by Abdulrahman Ikram Siddiq, 2011
# Initialize
counter = 0
label = [9999] * len(m_labels)
duration = [9999] * len(m_labels)
# Starting the lists
label[counter] = m_labels[counter] # first element
duration[counter] = 1 # first element
for i in range(1,len(m_labels)):
# Add 1 to duration, if the next element is the same as current microstate
if m_labels[i-1] == m_labels[i]:
duration[counter] += 1
# If it is not the same, get the new microstate label and restart duration
else:
counter += 1
label[counter] = m_labels[i]
duration[counter] = 1
# Trim the unused parts of the list
label = label[:counter+1]
duration = duration[:counter+1]
assert not any(np.array(label) == 9999) # all fillers removed
assert not any(np.array(duration) == 9999) # all fillers removed
return np.array(label), np.array(duration)
def single_micro_fit_all_feature_computation(i, n_maps, microstate_results, trialinfo_list, sfreq, event_id,
single_brain_event_id):
"""
Estimates the common (intrabrain) microstate features:
1. Average duration a given microstate remains stable (Dur)
2. Frequency occurrence, independent of individual duration (Occ)
Average number of times a microstate becomes dominant per second
3. Ratio of total Time Covered (TCo)
4. Transition probabilities (TMx)
5. Ratio of shannon entropy relative to theoretical max chaos (Ent)
Parameters
----------
i : int
The index.
n_maps : int
The number of maps (clusters) used.
microstate_results : list
The estimated microstates.
trialinfo_list : list
List with trial informations.
sfreq : int
The sampling frequency.
event_id : dict
The mappings between event id and condition.
single_brain_event_id : dict
The renamed mappings between event id and condition for single-brain
analysis.
Returns
-------
m_labels : np.array
The microstate sequence (labels) time series in the format (epoch, time).
events : pd.DataFrame
The events corresponding to each epoch.
MFeatures : list
List of arrays of each microstate feature.
"""
# Get environmental variables
Subject_id = os.environ.get('Subject_id')
# Get the microstate labels
sub_idx = microstate_results[5]
subject_indices = sub_idx[i], sub_idx[i+1]
m_labels = microstate_results[1][subject_indices[0]:subject_indices[1]]
# Get the trialinfo with conditions
# Notice that ppn2 events have been corrected, so we are using separate
# observer and actor conditions
# and also separate follower and leader conditions
Subject0, trialinfo = trialinfo_list[i]
assert Subject_id[i] == Subject0
assert len(trialinfo) == len(m_labels)/sfreq
# Convert to dataframe
event_id_inv = {v: k for k, v in event_id.items()} # Inverse the event id
events = pd.DataFrame(trialinfo,columns=["Event_id","Trial_number","Trial_start_time"])
events["Event_label"] = events["Event_id"].replace(event_id_inv)
events = events.reset_index().rename(columns={"index":"Epoch_idx"})
# Reshape m_labels to (epoch, time)
m_labels = m_labels.reshape(len(trialinfo),sfreq)
# Remove pre-trial epochs
pre_trial_epochs = events["Trial_start_time"] < 0
m_labels = m_labels[np.invert(pre_trial_epochs)]
events = events.loc[np.invert(pre_trial_epochs)].reset_index(drop=True)
events["Epoch_idx"] = events.index
# Initialize arrays
Dur_arr = np.zeros((len(single_brain_event_id),n_maps)); Dur_arr.fill(np.nan)
Occ_arr = np.zeros((len(single_brain_event_id),n_maps)); Occ_arr.fill(np.nan)
TCo_arr = np.zeros((len(single_brain_event_id),n_maps)); TCo_arr.fill(np.nan)
TMx_arr = np.zeros((len(single_brain_event_id),n_maps,n_maps))
Ent_arr = np.zeros(len(single_brain_event_id))
for e in range(len(single_brain_event_id)):
ev_idx = list(single_brain_event_id.values())[e]
ep_idx = events["Epoch_idx"][events["Event_id"] == ev_idx]
trial_numbers0 = np.unique(events["Trial_number"][ep_idx])
if len(trial_numbers0) > 8:
# Compute the first 8 trials and the last 8 separately, before
# taking the average to be consistent with asymmetric trials
Dur_arr0 = np.zeros((2,n_maps))
Occ_arr0 = np.zeros((2,n_maps))
TCo_arr0 = np.zeros((2,n_maps))
TMx_arr0 = np.zeros((2,n_maps,n_maps))
Ent_arr0 = np.zeros(2)
trial_numbers_split = np.array_split(trial_numbers0, 2)
# Array_split is used in cases where a trial might have been
# dropped, hence the split is only 8/7
for s in range(len(trial_numbers_split)):
ep_idx0 = events.loc[(events["Event_id"] == ev_idx)&
(events["Trial_number"].isin(trial_numbers_split[s])),
"Epoch_idx"]
m_labels0 = m_labels[ep_idx0,:]
m_labels0_flat = m_labels0.reshape(m_labels0.shape[0]*m_labels0.shape[1])
# Compute duration, occurrence and time covered
l_, d_ = microstate_run_length_encoding(m_labels0_flat)
# For each microstate
for ii in range(n_maps):
if np.isnan(np.nanmean(d_[l_==ii])):
# The specific microstate did not occur at all for this event
Dur_arr0[s,ii] = 0
Occ_arr0[s,ii] = 0
TCo_arr0[s,ii] = 0
else:
Dur_arr0[s,ii] = np.mean(d_[l_==ii]) * 1000/sfreq # convert to ms
Occ_arr0[s,ii] = len(d_[l_==ii])/len(d_) * sfreq
TCo_arr0[s,ii] = np.sum(d_[l_==ii])/np.sum(d_)
# Compute transition matrix
TMx_arr0[s] = T_empirical(m_labels0_flat, n_maps)
# Compute Shannon Entropy relative to max
Ent_arr0[s] = H_1(m_labels0_flat, n_maps)/np.log(float(n_maps))
# Average over the splits
Dur_arr[e] = np.mean(Dur_arr0, axis=0)
Occ_arr[e] = np.mean(Occ_arr0, axis=0)
TCo_arr[e] = np.mean(TCo_arr0, axis=0)
TMx_arr[e] = np.mean(TMx_arr0, axis=0)
Ent_arr[e] = np.mean(Ent_arr0, axis=0)
else:
m_labels0 = m_labels[ep_idx,:]
m_labels0_flat = m_labels0.reshape(m_labels0.shape[0]*m_labels0.shape[1])
# Compute duration, occurrence and time covered
l_, d_ = microstate_run_length_encoding(m_labels0_flat)
# For each microstate
for ii in range(n_maps):
if np.isnan(np.nanmean(d_[l_==ii])):
# The specific microstate did not occur at all for this event
Dur_arr[e,ii] = 0
Occ_arr[e,ii] = 0
TCo_arr[e,ii] = 0
else:
Dur_arr[e,ii] = np.mean(d_[l_==ii]) * 1000/sfreq # convert to ms
Occ_arr[e,ii] = len(d_[l_==ii])/len(d_) * sfreq
TCo_arr[e,ii] = np.sum(d_[l_==ii])/np.sum(d_)
# Compute transition matrix
TMx_arr[e] = T_empirical(m_labels0_flat, n_maps)
# Compute Shannon Entropy relative to max
Ent_arr[e] = H_1(m_labels0_flat, n_maps)/np.log(float(n_maps))
# Save all microstate features together
MFeatures = [Dur_arr,Occ_arr,TCo_arr,TMx_arr,Ent_arr]
return m_labels, events, MFeatures
def interbrain_microstate_run_length_encoding(m_labels1, m_labels2):
"""
Takes two 1D streams of microstate labels and returns the label
and duration for the common microstate. If there are no common
microstate, the label -1 is returned with a corresponding duration.
Adapted from RLE Matlab code by Abdulrahman Ikram Siddiq, 2011
"""
assert len(m_labels1) == len(m_labels2), "The provided microstate labels do not have equal length"
# Initialize
counter = 0
label = [9999] * len(m_labels1)
duration = [9999] * len(m_labels1)
# Starting the lists
if m_labels1[counter] == m_labels2[counter]:
label[counter] = m_labels1[counter]
else:
label[counter] = -1
duration[counter] = 1 # first element
for i in range(1,len(m_labels1)):
# If the previous timepoint was not common microstate, then add
# 1 to the duration if the next timepoint is also not common
if label[counter] == -1:
if m_labels1[i] != m_labels2[i]:
duration[counter] += 1
# If it becomes a common microstate, then get new microstate
# label and restart duration
elif m_labels1[i] == m_labels2[i]:
counter += 1
label[counter] = m_labels1[i]
duration[counter] = 1
# If the previous time point was a common microstate, then add
# 1 to duration if the next time point is also common and the same label
elif label[counter] != -1:
if m_labels1[i] == m_labels2[i]:
if label[counter] == m_labels1[i]:
duration[counter] += 1
# If still common, but a new label for both timeseries
# then restart with new label and duration
else:
counter += 1
label[counter] = m_labels1[i]
duration[counter] = 1
# If the two timeseries have different microstates, then
# set lable to -1 and restart duration
elif m_labels1[i] != m_labels2[i]:
counter += 1
label[counter] = -1
duration[counter] = 1
# Trim the unused parts of the list
label = label[:counter+1]
duration = duration[:counter+1]
assert not any(np.array(label) == 9999) # all fillers removed
assert not any(np.array(duration) == 9999) # all fillers removed
return np.array(label), np.array(duration)
def interbrain_T_matrix(m_labels1, m_labels2, n_maps):
"""
Takes two 1D microstate labels and returns the transition matrix
The first row and column correspond to label -1, which indicates
no common microstate
Adapted from von Wegner F and Laufs H, 2018
"""
assert len(m_labels1) == len(m_labels2), "The provided microstate labels do not have equal length"
T = np.zeros((n_maps+1, n_maps+1))
n = len(m_labels1)
for i in range(n-1):
# If common microstate, then use that label
if m_labels1[i] == m_labels2[i]:
row_idx = m_labels1[i]+1
# Or set row as 0, the not common microstate
else:
row_idx = 0
# If next state is also common, set it to that state
if m_labels1[i+1] == m_labels2[i+1]:
col_idx = m_labels1[i+1]+1
# Or set col as 0, if it is not common microstate
else:
col_idx = 0
# Add 1 count to the transition matrix
T[row_idx,col_idx] += 1.0
assert n - np.sum(T) == 1 # check whether all transitions have been counted
# Normalize row sums to 1
T /= T.sum(axis=1, keepdims=True)
return T
def interbrain_microstate_feature_computation(i, n_maps, microstate_results, trialinfo_list, sfreq, event_id,
collapsed_event_id):
"""
Interbrain features:
1. Average duration of common interbrain microstates (IBDur)
2. Frequency occurrence of common interbrain microstates in the pair (IBOcc)
3. Ratio of total time covered by interbrain common microstates in the pair (IBCov)
4. Transition probability towards common interbrain microstates in the pair (IBTMx)
5. Ratio of joint shannon entropy relative to theoretical max chaos (IBEnt)
Parameters
----------
i : int
The index.
n_maps : int
The number of maps (clusters) used.
microstate_results : list
The estimated microstates.
trialinfo_list : list
List with trial informations.
sfreq : int
The sampling frequency.
event_id : dict
The mappings between event id and condition.
collapsed_brain_event_id : dict
The renamed mappings between event id and collapsed conditions
Returns
-------
m_labels : list of two np.array
The microstate sequence (labels) time series in the format (epoch, time).
events : list of two pd.DataFrame
The events corresponding to each epoch.
MFeatures : list
List of arrays of each microstate feature.
"""
# Here i refers to the pair in range(n_subjects//2)
sub_idx = microstate_results[5]
# Get the microstate labels and events for participant 1
subject_indices1 = sub_idx[2*i], sub_idx[2*i+1]
m_labels1 = microstate_results[1][subject_indices1[0]:subject_indices1[1]]
# Get the trialinfo with conditions
Subject1, trialinfo1 = trialinfo_list[2*i]
# Convert to dataframe
event_id_inv = {v: k for k, v in event_id.items()} # Inverse the event id
events1 = pd.DataFrame(trialinfo1,columns=["Event_id","Trial_number","Trial_start_time"])
events1["Event_label"] = events1["Event_id"].replace(event_id_inv)
events1 = events1.reset_index().rename(columns={"index":"Epoch_idx"})
# Reshape m_labels to (epoch, time)
m_labels1 = m_labels1.reshape(len(trialinfo1),sfreq)
# Get the microstate labels and events for participant 2
subject_indices2 = sub_idx[2*i+1], sub_idx[2*i+2]
m_labels2 = microstate_results[1][subject_indices2[0]:subject_indices2[1]]
# Get the trialinfo with conditions
Subject2, trialinfo2 = trialinfo_list[2*i+1]
# Convert to dataframe
events2 = pd.DataFrame(trialinfo2,columns=["Event_id","Trial_number","Trial_start_time"])
events2["Event_label"] = events2["Event_id"].replace(event_id_inv)
events2 = events2.reset_index().rename(columns={"index":"Epoch_idx"})
# Reshape m_labels to (epoch, time)
m_labels2 = m_labels2.reshape(len(trialinfo2),sfreq)
# check that the participants from a pair was loaded
assert Subject2-1 == Subject1
# Check that the same amount of event types are present
assert trialinfo1[-1,1] == trialinfo2[-1,1]
# Synchronize the events from the pair based on timing info
# By trimming the epochs to only include the epochs that are present
# in both participants of the pair
# Initialize with an array filled with a unique number not in use
sync_m_labels1 = np.zeros_like(m_labels1); sync_m_labels1.fill(9999)
sync_m_labels2 = np.zeros_like(m_labels2); sync_m_labels2.fill(9999)
for t in np.unique(trialinfo1[:,1]):
t_idx1 = np.where(trialinfo1[:,1] == t)[0]
t_idx2 = np.where(trialinfo2[:,1] == t)[0]
# Get the timings for the epochs for the specific trial t
t_timings1 = trialinfo1[t_idx1,2]
t_timings2 = trialinfo2[t_idx2,2]
timings_intersect = np.intersect1d(t_timings1,t_timings2)
# Get the indices where the timings matches
t_idx_match1 = t_idx1[pd.Series(t_timings1).isin(timings_intersect)]
t_idx_match2 = t_idx2[pd.Series(t_timings2).isin(timings_intersect)]
# Get the actual values from the synchronized epochs
sync_m_labels1[t_idx_match1] = m_labels1[t_idx_match1]
sync_m_labels2[t_idx_match2] = m_labels2[t_idx_match2]
# Find the epochs that were asynchronous, which have to be trimmed
asynch_epochs1 = np.unique(np.where(sync_m_labels1==9999)[0])
asynch_epochs2 = np.unique(np.where(sync_m_labels2==9999)[0])
# Trim/delete the asynchronous epochs
sync_m_labels1 = np.delete(sync_m_labels1,asynch_epochs1,axis=0)
sync_m_labels2 = np.delete(sync_m_labels2,asynch_epochs2,axis=0)
assert len(sync_m_labels1) == len(sync_m_labels2) # check the amount of synchronized epochs are equal
# Fix events
sync_events1 = events1.drop(asynch_epochs1,axis=0).reset_index(drop=True)
sync_events2 = events2.drop(asynch_epochs2,axis=0).reset_index(drop=True)
# Since they are synchronized already, just take the first and fix
# epoch idx column with the new idx for the synchronized labels
events = sync_events1
events["Epoch_idx"] = events.index
# Notice that for the intrabrain fit all alpha v6 I only corrected
# ppn2. So by taking ppn1 I get the original event labels.
# Update in v7
# # Collapse event_id 6 and 7 to 4 and 5
# events.loc[events["Event_id"] == 6,["Event_id","Event_label"]] = [4, "observe, actor"]
# events.loc[events["Event_id"] == 7,["Event_id","Event_label"]] = [5, "imitate, leader"]
# Due to the hard constraint for both participants being in the same
# microstate. It does not matter who will be treated as ppn1 and ppn2
# since the prototypical microstate topographies are exactly the same!
# Remove pre-trial epochs
pre_trial_epochs = events["Trial_start_time"] < 0
sync_m_labels1 = sync_m_labels1[np.invert(pre_trial_epochs)]
sync_m_labels2 = sync_m_labels2[np.invert(pre_trial_epochs)]
events = events.loc[np.invert(pre_trial_epochs)].reset_index(drop=True)
events["Epoch_idx"] = events.index
# Pre-allocate memory
Dur_arr = np.zeros((len(event_id),n_maps+1)); Dur_arr.fill(np.nan)
Occ_arr = np.zeros((len(event_id),n_maps+1)); Occ_arr.fill(np.nan)
TCo_arr = np.zeros((len(event_id),n_maps+1)); TCo_arr.fill(np.nan)
TMx_arr = np.zeros((len(event_id),n_maps+1,n_maps+1))
Ent_arr = np.zeros(len(event_id))
for e in range(len(event_id)):
ev_idx = list(event_id.values())[e]
ep_idx = events["Epoch_idx"][events["Event_id"] == ev_idx]
trial_numbers0 = np.unique(events["Trial_number"][ep_idx])
if len(trial_numbers0) > 8:
# Compute the first 8 trials and the last 8 separately, before
# taking the average to be consistent with asymmetric trials
Dur_arr0 = np.zeros((2,n_maps+1))
Occ_arr0 = np.zeros((2,n_maps+1))
TCo_arr0 = np.zeros((2,n_maps+1))
TMx_arr0 = np.zeros((2,n_maps+1,n_maps+1))
Ent_arr0 = np.zeros(2)
trial_numbers_split = np.array_split(trial_numbers0, 2)
# Array_split is used in cases where a trial might have been
# dropped, hence the split is only 8/7
for s in range(len(trial_numbers_split)):
ep_idx0 = events.loc[(events["Event_id"] == ev_idx)&
(events["Trial_number"].isin(trial_numbers_split[s])),
"Epoch_idx"]
# Get the microstate labels
m_labels10 = sync_m_labels1[ep_idx0,:]
m_labels20 = sync_m_labels2[ep_idx0,:]
# Flatten the labels
m_labels10_flat = m_labels10.reshape(m_labels10.shape[0]*m_labels10.shape[1])
m_labels20_flat = m_labels20.reshape(m_labels20.shape[0]*m_labels20.shape[1])
# Compute average duration of common microstate
# Output: label and duration of common microstate. Label -1 is used
# for not common microstate
l_, d_ = interbrain_microstate_run_length_encoding(m_labels10_flat,m_labels20_flat)
# For each microstate
for ii in range(n_maps+1):
if np.isnan(np.nanmean(d_[l_==ii-1])):
# The specific microstate did not occur at all for this event
Dur_arr0[s,ii] = 0
Occ_arr0[s,ii] = 0
TCo_arr0[s,ii] = 0
else:
Dur_arr0[s,ii] = np.mean(d_[l_==ii-1]) * 1000/sfreq # convert to ms
Occ_arr0[s,ii] = len(d_[l_==ii-1])/len(d_) * sfreq
TCo_arr0[s,ii] = np.sum(d_[l_==ii-1])/np.sum(d_)
# Compute transition matrix
TMx_arr0[s] = interbrain_T_matrix(m_labels10_flat,m_labels20_flat,n_maps)
# Compute Joint Shannon Entropy relative to max
# Max is the sum of max individual entropies
Ent_arr0[s] = H_2(m_labels10_flat,m_labels20_flat,n_maps)/(2*np.log(float(n_maps)))
# Average over the splits
Dur_arr[e] = np.mean(Dur_arr0, axis=0)
Occ_arr[e] = np.mean(Occ_arr0, axis=0)
TCo_arr[e] = np.mean(TCo_arr0, axis=0)
TMx_arr[e] = np.mean(TMx_arr0, axis=0)
Ent_arr[e] = np.mean(Ent_arr0, axis=0)
else:
# Get the microstate labels
m_labels10 = sync_m_labels1[ep_idx,:]
m_labels20 = sync_m_labels2[ep_idx,:]
# Flatten the labels
m_labels10_flat = m_labels10.reshape(m_labels10.shape[0]*m_labels10.shape[1])
m_labels20_flat = m_labels20.reshape(m_labels20.shape[0]*m_labels20.shape[1])
# Compute average duration of common microstate
# Output: label and duration of common microstate. Label -1 is used
# for not common microstate
l_, d_ = interbrain_microstate_run_length_encoding(m_labels10_flat,m_labels20_flat)
# For each microstate
for ii in range(n_maps+1):
if np.isnan(np.nanmean(d_[l_==ii-1])):
# The specific microstate did not occur at all for this event
Dur_arr[e,ii] = 0
Occ_arr[e,ii] = 0
TCo_arr[e,ii] = 0
else:
Dur_arr[e,ii] = np.mean(d_[l_==ii-1]) * 1000/sfreq # convert to ms
Occ_arr[e,ii] = len(d_[l_==ii-1])/len(d_) * sfreq
TCo_arr[e,ii] = np.sum(d_[l_==ii-1])/np.sum(d_)
# Compute transition matrix
TMx_arr[e] = interbrain_T_matrix(m_labels10_flat,m_labels20_flat,n_maps)
# Compute Joint Shannon Entropy relative to max
# Max is the sum of max individual entropies
Ent_arr[e] = H_2(m_labels10_flat,m_labels20_flat,n_maps)/(2*np.log(float(n_maps)))
# Combine all features in a list
MFeatures = [Dur_arr,Occ_arr,TCo_arr,TMx_arr,Ent_arr]
# Update in v7 - Collapse after computations in single events
Dur_arr2 = np.zeros((len(collapsed_event_id),n_maps+1))
Occ_arr2 = np.zeros((len(collapsed_event_id),n_maps+1))
TCo_arr2 = np.zeros((len(collapsed_event_id),n_maps+1))
TMx_arr2 = np.zeros((len(collapsed_event_id),n_maps+1,n_maps+1))
Ent_arr2 = np.zeros(len(collapsed_event_id))
MFeatures2 = [Dur_arr2,Occ_arr2,TCo_arr2,TMx_arr2,Ent_arr2]
for f in range(len(MFeatures2)):
tmp_feat = MFeatures[f]
tmp_feat2 = MFeatures2[f]
for e in range(len(collapsed_event_id)):
ee = list(collapsed_event_id.keys())[e]
if (ee == 'observer_actor'):
old_ev_idx1 = list(event_id.keys()).index("observe, actor")
old_ev_idx2 = list(event_id.keys()).index("observe, observer")
tmp_feat2[e] = np.mean([tmp_feat[old_ev_idx1],tmp_feat[old_ev_idx2]], axis=0)
elif ee == 'follower_leader':
old_ev_idx1 = list(event_id.keys()).index("imitate, leader")
old_ev_idx2 = list(event_id.keys()).index("imitate, follower")
tmp_feat2[e] = np.mean([tmp_feat[old_ev_idx1],tmp_feat[old_ev_idx2]], axis=0)
else:
new_ev_idx = list(collapsed_event_id.values())[e]
old_ev_idx = list(event_id.values()).index(new_ev_idx)
tmp_feat2[e] = tmp_feat[old_ev_idx]
return [sync_m_labels1, sync_m_labels2], [sync_events1, sync_events2], MFeatures2
def get_synch_events_from_pairs(i, trialinfo1, trialinfo2, event_id):
""" Get the synchronized events for each pair """
# Get environmental variables
Subject_id = os.environ.get('Subject_id')
# Check that the participants from a pair was loaded
assert Subject_id[2*i+1]-1 == Subject_id[2*i]
# Synchronize the events from the pair based on timing info
# By trimming the epochs to only include the epochs that are present
# in both participants of the pair
event_id_inv = {v: k for k, v in event_id.items()} # Inverse the event id
events1 = pd.DataFrame(trialinfo1,columns=["Event_id","Trial_number","Trial_start_time"])
events1["Event_label"] = events1["Event_id"].replace(event_id_inv)
events1 = events1.reset_index().rename(columns={"index":"Epoch_idx"})
events2 = pd.DataFrame(trialinfo2,columns=["Event_id","Trial_number","Trial_start_time"])
events2["Event_label"] = events2["Event_id"].replace(event_id_inv)
events2 = events2.reset_index().rename(columns={"index":"Epoch_idx"})
# Concatenate all the synched events
sync_events1 = np.zeros((len(events1)))
sync_events2 = np.zeros((len(events2)))
for t in np.unique(trialinfo1[:,1]):
t_idx1 = np.where(trialinfo1[:,1] == t)[0]
t_idx2 = np.where(trialinfo2[:,1] == t)[0]
# Get the timings for the epochs for the specific trial t
t_timings1 = trialinfo1[t_idx1,2]
t_timings2 = trialinfo2[t_idx2,2]
timings_intersect = np.intersect1d(t_timings1,t_timings2)
# Get the indices where the timings matches
t_idx_match1 = t_idx1[pd.Series(t_timings1).isin(timings_intersect)]
t_idx_match2 = t_idx2[pd.Series(t_timings2).isin(timings_intersect)]
# Save the trials that should be kept
sync_events1[t_idx_match1] = 1
sync_events2[t_idx_match2] = 1
# Find the epochs that were asynchronous, which have to be trimmed
asynch_epochs1 = np.where(sync_events1==0)[0]
asynch_epochs2 = np.where(sync_events2==0)[0]
# Trim/delete the asynchronous epochs
events1 = events1.drop(asynch_epochs1,axis=0).reset_index(drop=True)
events2 = events2.drop(asynch_epochs2,axis=0).reset_index(drop=True)
assert len(events1) == len(events2) # check the amount of synchronized epochs are equal
return events1, events2
def prepare_2P_micro_arr_collapsed_events(i, sfreq, event_id, freq_range=None, standardize:bool=True):
# Here i refers to the pair in range(n_subjects//2)
# Load epochs
epoch1, trialinfo1 = load_epoch_from_fieldtrip(2*i)
epoch2, trialinfo2 = load_epoch_from_fieldtrip(2*i+1)
# Ensure it is averaged referenced
epoch1 = epoch1.set_eeg_reference("average")
epoch2 = epoch2.set_eeg_reference("average")
# Get numpy arrays
micro_data1 = epoch1.get_data() # get data
micro_data2 = epoch2.get_data() # get data
# Get only the synchronized epochs
events = get_synch_events_from_pairs(i, trialinfo1, trialinfo2, event_id)
micro_data1 = micro_data1[events[0]["Epoch_idx"]]
micro_data2 = micro_data2[events[1]["Epoch_idx"]]
assert micro_data1.shape == micro_data2.shape
# Get event labels for each epoch
# Since they are already synchronized I just take the first
event_labels0 = events[0]["Event_label"]
if standardize == True:
# Standardize data - will make EEG amplitudes comparable between
# recordings/trials. Standardized along the ch axis, meaning the mean
# and std of the topomap at each timepoint is normalized
# It will be normalized separately for each participant
# in order to ensure both participants are treated equally by the
# kmeans
micro_data10 = micro_data1 - micro_data1.mean(axis=1, keepdims=True)
micro_data10 /= micro_data10.std(axis=1, keepdims=True)
micro_data20 = micro_data2 - micro_data2.mean(axis=1, keepdims=True)
micro_data20 /= micro_data20.std(axis=1, keepdims=True)
elif standardize == False:
micro_data10 = micro_data1
micro_data20 = micro_data2
# Concatenate along the channel axis, to treat the paired EEG as one entity
micro_data = np.concatenate([micro_data10,micro_data20],axis=1)
# Flip the micro_data between ppn1 and ppn2 to fix first microstate
# always being observer/follower, and second microstate being actor/leader
cond6_idx = event_labels0 == "observe, observer"
cond7_idx = event_labels0 == "imitate, follower"
cond67_idx = cond6_idx+cond7_idx
micro_data[cond67_idx] = np.concatenate([micro_data20[cond67_idx],
micro_data10[cond67_idx]],axis=1)
# Transform data to correct shape
arr_shape = micro_data.shape # get shape
micro_data = micro_data.swapaxes(1,2) # swap ch and time axis
micro_data = micro_data.reshape(arr_shape[0]*arr_shape[2],arr_shape[1]) # reshape by combining epochs and times
# Filter the data
if freq_range == None:
micro_data_filtered = micro_data # do not perform filtering
elif not freq_range == None:
# Notice it is done only after combining epochs and time, as filter length
# would be too long for 1s epochs. The filter function wants time on the last axis
micro_data = micro_data.transpose()
micro_data_filtered = mne.filter.filter_data(micro_data, sfreq, freq_range[0], freq_range[1])
# Reverse the shape
micro_data_filtered = micro_data_filtered.transpose()
return micro_data_filtered, [trialinfo1, trialinfo2], events
def plot_dualmicro(n_maps, maps, gev, epoch_info, vlims=(None, None)):
# Split the map from the two participants
n_channels = epoch_info["nchan"]
maps1, maps2, _ = np.split(maps, [n_channels,2*n_channels], axis=1)
fig, axarr = plt.subplots(2, n_maps, figsize=(20,10))
fig.patch.set_facecolor('white')
for imap in range(n_maps):
mne.viz.plot_topomap(maps1[imap,:], pos = epoch_info, vlim = vlims, axes = axarr[0,imap]) # plot
axarr[0,imap].set_title("GEV: {:.2f}".format(gev[imap]), fontsize=16, fontweight="bold") # title
mne.viz.plot_topomap(maps2[imap,:], pos = epoch_info, vlim = vlims, axes = axarr[1,imap])
fig.suptitle("Microstates: {}".format(n_maps), fontsize=20, fontweight="bold")
plt.tight_layout()
return fig
def sign_swap_microstates(sign_swap, maps, n_maps, n_channels):
# Split maps
maps1, maps2, _ = np.split(maps, [n_channels,2*n_channels], axis=1)
for m in range(n_maps):
# Sign swap each
maps1[m] *= sign_swap[0][m]
maps2[m] *= sign_swap[1][m]
# Concatenate maps
maps = np.concatenate([maps1,maps2],axis=1)
return maps
def dualmicro_fit_all_feature_computation(i, n_maps, microstate_results, trialinfo_list, sfreq, event_id,
collapsed_event_id):
"""
Overview of common microstate features:
1. Average duration a given microstate remains stable (Dur)
2. Frequency occurrence, independent of individual duration (Occ)
Average number of times a microstate becomes dominant per second
3. Ratio of total Time Covered (TCo)
4. Transition probabilities (TMx)
5. Ratio of shannon entropy relative to theoretical max chaos (Ent)
Parameters
----------
i : int
The index.
n_maps : int
The number of maps (clusters) used.
microstate_results : list
The estimated microstates.
trialinfo_list : list
List with trial informations.
sfreq : int
The sampling frequency.
event_id : dict
The mappings between event id and condition.
collapsed_brain_event_id : dict
The renamed mappings between event id and collapsed conditions
Returns
-------
m_labels : list of two np.array
The microstate sequence (labels) time series in the format (epoch, time).
events : list of two pd.DataFrame
The events corresponding to each epoch.
MFeatures : list
List of arrays of each microstate feature.
"""
pair_idx = microstate_results[5]
pair_indices = pair_idx[i], pair_idx[i+1]
m_labels = microstate_results[1][pair_indices[0]:pair_indices[1]]
events = trialinfo_list[2][i]
# Since they are synchronized already, just take the first and fix
# epoch idx column with the new idx for the synchronized labels
events = events[0]
# Update in v7
# # Collapse event_id 6 and 7 to 4 and 5
# events.loc[events["Event_id"] == 6,["Event_id","Event_label"]] = [4, "observe, actor"]
# events.loc[events["Event_id"] == 7,["Event_id","Event_label"]] = [5, "imitate, leader"]
# The microstate clustering was performed on flipped (collapsed) events,
# but I will compute the features on the 8 trials to avoid the flipping
# effect and then collapse by averaging afterwards
# Reshape m_labels to (epoch, time)
m_labels = m_labels.reshape(len(events),sfreq)
# Remove pre-trial epochs
pre_trial_epochs = events["Trial_start_time"] < 0
m_labels = m_labels[np.invert(pre_trial_epochs)]
events = events.loc[np.invert(pre_trial_epochs)].reset_index(drop=True)
events["Epoch_idx"] = events.index
# Pre-allocate memory
Dur_arr = np.zeros((len(event_id),n_maps)); Dur_arr.fill(np.nan)
Occ_arr = np.zeros((len(event_id),n_maps)); Occ_arr.fill(np.nan)
TCo_arr = np.zeros((len(event_id),n_maps)); TCo_arr.fill(np.nan)
TMx_arr = np.zeros((len(event_id),n_maps,n_maps))
Ent_arr = np.zeros(len(event_id))
for e in range(len(event_id)):
ev_idx = list(event_id.values())[e]
ep_idx = events["Epoch_idx"][events["Event_id"] == ev_idx]
trial_numbers0 = np.unique(events["Trial_number"][ep_idx])
if len(trial_numbers0) > 8:
# Compute the first 8 trials and the last 8 separately, before
# taking the average to be consistent with asymmetric trials
Dur_arr0 = np.zeros((2,n_maps))
Occ_arr0 = np.zeros((2,n_maps))
TCo_arr0 = np.zeros((2,n_maps))
TMx_arr0 = np.zeros((2,n_maps,n_maps))
Ent_arr0 = np.zeros(2)
trial_numbers_split = np.array_split(trial_numbers0, 2)
# Array_split is used in cases where a trial might have been
# dropped, hence the split is only 8/7
for s in range(len(trial_numbers_split)):
ep_idx0 = events.loc[(events["Event_id"] == ev_idx)&
(events["Trial_number"].isin(trial_numbers_split[s])),
"Epoch_idx"]
m_labels0 = m_labels[ep_idx0,:]
m_labels0_flat = m_labels0.reshape(m_labels0.shape[0]*m_labels0.shape[1])
# Compute duration, occurrence and time covered
l_, d_ = microstate_run_length_encoding(m_labels0_flat)
# For each microstate
for ii in range(n_maps):
if np.isnan(np.nanmean(d_[l_==ii])):
# The specific microstate did not occur at all for this event
Dur_arr0[s,ii] = 0
Occ_arr0[s,ii] = 0
TCo_arr0[s,ii] = 0
else:
Dur_arr0[s,ii] = np.mean(d_[l_==ii]) * 1000/sfreq # convert to ms
Occ_arr0[s,ii] = len(d_[l_==ii])/len(d_) * sfreq
TCo_arr0[s,ii] = np.sum(d_[l_==ii])/np.sum(d_)
# Compute transition matrix
TMx_arr0[s] = T_empirical(m_labels0_flat, n_maps)
# Compute Shannon Entropy relative to max
Ent_arr0[s] = H_1(m_labels0_flat, n_maps)/np.log(float(n_maps))
# Average over the splits
Dur_arr[e] = np.mean(Dur_arr0, axis=0)
Occ_arr[e] = np.mean(Occ_arr0, axis=0)
TCo_arr[e] = np.mean(TCo_arr0, axis=0)
TMx_arr[e] = np.mean(TMx_arr0, axis=0)
Ent_arr[e] = np.mean(Ent_arr0, axis=0)
else:
m_labels0 = m_labels[ep_idx,:]
m_labels0_flat = m_labels0.reshape(m_labels0.shape[0]*m_labels0.shape[1])
# Compute duration, occurrence and time covered
l_, d_ = microstate_run_length_encoding(m_labels0_flat)
# For each microstate
for ii in range(n_maps):
if np.isnan(np.nanmean(d_[l_==ii])):
# The specific microstate did not occur at all for this event
Dur_arr[e,ii] = 0
Occ_arr[e,ii] = 0
TCo_arr[e,ii] = 0
else:
Dur_arr[e,ii] = np.mean(d_[l_==ii]) * 1000/sfreq # convert to ms
Occ_arr[e,ii] = len(d_[l_==ii])/len(d_) * sfreq
TCo_arr[e,ii] = np.sum(d_[l_==ii])/np.sum(d_)
# Compute transition matrix
TMx_arr[e] = T_empirical(m_labels0_flat, n_maps)
# Compute Shannon Entropy relative to max
Ent_arr[e] = H_1(m_labels0_flat, n_maps)/np.log(float(n_maps))
# Combine all features in a list
MFeatures = [Dur_arr,Occ_arr,TCo_arr,TMx_arr,Ent_arr]
# Update in v7 - Collapse after computations in single events
Dur_arr2 = np.zeros((len(collapsed_event_id),n_maps))
Occ_arr2 = np.zeros((len(collapsed_event_id),n_maps))
TCo_arr2 = np.zeros((len(collapsed_event_id),n_maps))
TMx_arr2 = np.zeros((len(collapsed_event_id),n_maps,n_maps))
Ent_arr2 = np.zeros(len(collapsed_event_id))
MFeatures2 = [Dur_arr2,Occ_arr2,TCo_arr2,TMx_arr2,Ent_arr2]
for f in range(len(MFeatures2)):
tmp_feat = MFeatures[f]
tmp_feat2 = MFeatures2[f]
for e in range(len(collapsed_event_id)):
ee = list(collapsed_event_id.keys())[e]
if (ee == 'observer_actor'):
old_ev_idx1 = list(event_id.keys()).index("observe, actor")
old_ev_idx2 = list(event_id.keys()).index("observe, observer")
tmp_feat2[e] = np.mean([tmp_feat[old_ev_idx1],tmp_feat[old_ev_idx2]], axis=0)
elif ee == 'follower_leader':
old_ev_idx1 = list(event_id.keys()).index("imitate, leader")
old_ev_idx2 = list(event_id.keys()).index("imitate, follower")
tmp_feat2[e] = np.mean([tmp_feat[old_ev_idx1],tmp_feat[old_ev_idx2]], axis=0)
else:
new_ev_idx = list(collapsed_event_id.values())[e]
old_ev_idx = list(event_id.values()).index(new_ev_idx)
tmp_feat2[e] = tmp_feat[old_ev_idx]
return m_labels, events, MFeatures2
def load_microstate_arrays(i, standardize:bool=True):
""" Loads the EEG data and convert to np.array """
epoch1, trialinfo1 = load_epoch_from_fieldtrip(i)
# Ensure it is averaged referenced
epoch1 = epoch1.set_eeg_reference("average")
# Get numpy arrays
micro_data1 = epoch1.get_data() # get data
if standardize == True:
# Standardize data - will make EEG amplitudes comparable between
# recordings/trials. Standardized along the ch axis, meaning the mean
# and std of the topomap at each timepoint is normalized
# It will be normalized separately for each participant
micro_data10 = micro_data1 - micro_data1.mean(axis=1, keepdims=True)
micro_data10 /= micro_data10.std(axis=1, keepdims=True)
elif standardize == False:
micro_data10 = micro_data1
return micro_data10, trialinfo1
def get_synch_events_from_pseudo_pairs(trialinfo1, trialinfo2, event_id):
"""
Takes two subject indices from a pseudo pair and align their events (based
on when they occur, e.g. first coupled in ppn1 with first coupled in ppn2)
After their microstates have been synchronized, the microstate features
are computed to serve as baseline as a non-interacting pair
"""
# Synchronize the events from the pair based on timing info
# By trimming the epochs to only include the epochs that are present
# in both participants of the pair
event_id_inv = {v: k for k, v in event_id.items()} # Inverse the event id
events1 = pd.DataFrame(trialinfo1,columns=["Event_id","Trial_number","Trial_start_time"])
events1["Event_label"] = events1["Event_id"].replace(event_id_inv)
events1 = events1.reset_index().rename(columns={"index":"Epoch_idx"})
events2 = pd.DataFrame(trialinfo2,columns=["Event_id","Trial_number","Trial_start_time"])
events2["Event_label"] = events2["Event_id"].replace(event_id_inv)
events2 = events2.reset_index().rename(columns={"index":"Epoch_idx"})
# I should just make sure to align the timings of the trials for each event type
counter_idx = 0
# Initialize events as dataframe filled with unique number
sync_events1 = events1.applymap(lambda x: 9999)
sync_events2 = events2.applymap(lambda x: 9999)
for e in range(len(event_id)):
# Get event number for the specific event
ev_idx = list(event_id.values())[e]
# Get the indices corresponding to the event for each participant
ep_idx1 = events1["Epoch_idx"][events1["Event_id"] == ev_idx]
ep_idx2 = events2["Epoch_idx"][events2["Event_id"] == ev_idx]
# Get the first trial from ppn1 and ppn2 that correspond to the event
event_trial_idx1 = np.unique(trialinfo1[ep_idx1][:,1])
event_trial_idx2 = np.unique(trialinfo2[ep_idx2][:,1])
if len(event_trial_idx1) != len(event_trial_idx2):
print(f"Not equal number of {list(event_id.keys())[e]} trials")
min_trials = np.min([len(event_trial_idx1),len(event_trial_idx2)])
print(f"Using the first {min_trials} trials")
event_trial_idx1 = event_trial_idx1[:min_trials]
event_trial_idx2 = event_trial_idx2[:min_trials]
for t1, t2 in zip(event_trial_idx1,event_trial_idx2):
t_idx1 = np.where(trialinfo1[:,1] == t1)[0]
t_idx2 = np.where(trialinfo2[:,1] == t2)[0]
# Get the timings for the epochs for the specific trial t
t_timings1 = trialinfo1[t_idx1,2]
t_timings2 = trialinfo2[t_idx2,2]
timings_intersect = np.intersect1d(t_timings1,t_timings2)
# Get the indices where the timings matches
t_idx_match1 = t_idx1[pd.Series(t_timings1).isin(timings_intersect)]
t_idx_match2 = t_idx2[pd.Series(t_timings2).isin(timings_intersect)]
# Save the event info
sync_events1.iloc[counter_idx:(counter_idx+len(timings_intersect))] = events1.iloc[t_idx_match1]
sync_events2.iloc[counter_idx:(counter_idx+len(timings_intersect))] = events2.iloc[t_idx_match2]
# Move counter
counter_idx += len(timings_intersect)
# Find the epochs that were asynchronous, which have to be trimmed
asynch_epochs1 = np.unique(np.where(sync_events1==9999)[0])
asynch_epochs2 = np.unique(np.where(sync_events2==9999)[0])
# Fix events
sync_events1 = sync_events1.drop(asynch_epochs1,axis=0).reset_index(drop=True)
sync_events2 = sync_events2.drop(asynch_epochs2,axis=0).reset_index(drop=True)
assert len(sync_events1) == len(sync_events2) # check the amount of synchronized epochs are equal
return [sync_events1, sync_events2]
def combine_two_person_microstate_arrays(micro_data1, micro_data2, events, sfreq, freq_range=None):
""" Combine two EEG array data into one array for microstate estimation """
# Get the synchronized epochs
micro_data1 = micro_data1[events[0]["Epoch_idx"]]
micro_data2 = micro_data2[events[1]["Epoch_idx"]]
# Get event labels for each epoch
# Since they are already synchronized I just take the first
event_labels0 = events[0]["Event_label"]
# Concatenate along the channel axis, to treat the paired EEG as one entity
micro_data = np.concatenate([micro_data1,micro_data2],axis=1)
### Update in v4.1
# Flip the micro_data between ppn1 and ppn2 to fix first microstate
# always being observer/follower, and second microstate being actor/leader
cond6_idx = event_labels0 == "observe, observer"