Merge remote-tracking branch 'refs/remotes/origin/master'

FeatureEstimation.py

@@ -36,14 +36,14 @@ the different parts easier (Default shortcut: Ctrl + Shift + O)
 
 # Set working directory
 import os
-wkdir = "/Users/benj3542/Desktop/Uni/Noter/Semester_6/Bachelor/resting-state-eeg-analysis/"
+wkdir = "/home/s200431"
 os.chdir(wkdir)
 
 # Load all libraries from the Preamble
 from Preamble import *
 
 # %% Load preprocessed epochs and questionnaire data
-load_path = "./PreprocessedData"
+load_path = "home/s200431/PreprocessedData"
 
 # Get filenames
 files = []
@@ -79,6 +79,9 @@ Drop_epochs_df = Drop_epochs_df.sort_values(by=["Subject_ID"]).reset_index(drop=
 ### Load questionnaire data
 # For the purposes of this demonstration I will make a dummy dataframe
 # The original code imported csv files with questionnaire data and group status
+final_qdf = pd.read_csv(r'/data/raw/FOR_DTU/Questionnaires_for_DTU.csv')
+
+"""
 final_qdf = pd.DataFrame({"Subject_ID":Subject_id,
                           "Age":[23,26],
                           "Gender":[0,0],
@@ -87,10 +90,11 @@ final_qdf = pd.DataFrame({"Subject_ID":Subject_id,
                           "Q1":[1.2, 2.3],
                           "Q2":[1.7, 1.5],
                           "Qn":[2.1,1.0]})
+"""
 
 # Define cases as >= 44 total PCL
 # Type: numpy array with subject id
-cases = np.array(final_qdf["Subject_ID"][final_qdf["PCL_total"]>=44])
+cases = np.array(final_qdf["Subject_ID"][final_qdf["PCL_t7"]>=44])
 n_groups = 2
 Groups = ["CTRL", "PTSD"]
 
@@ -887,7 +891,7 @@ for i in range(len(order)):
         maps[i][m] *= sign_swap[i][m]
 
 # Plot the maps and save
-save_path = "/Users/benj3542/Desktop/Uni/Noter/Semester_6/Bachelor/resting-state-eeg-analysis/Figures/Microstates"
+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))

Preamble.py

@@ -29,14 +29,16 @@ import statsmodels # multipletest
 import fooof # Peak Alpha Freq and 1/f exponents
 import pandas as pd # Dataframes
 import seaborn as sns # Plotting library
-# import autoreject # Automatic EEG artifact detection
+from h5io import read_hdf5, write_hdf5 
+import autoreject # Automatic EEG artifact detection
 import mlxtend # Sequential Forward Selection
 
 from mne.time_frequency import *
+from mne.time_frequency import psd_multitaper
 from mne.preprocessing import (ICA, create_eog_epochs, create_ecg_epochs, corrmap)
 from mne.stats import spatio_temporal_cluster_test, permutation_cluster_test
 from mne.channels import find_ch_adjacency
-from mne.connectivity import spectral_connectivity
+#from mne.connectivity import spectral_connectivity
 
 import nitime.analysis as nta
 import nitime.timeseries as nts
@@ -61,6 +63,7 @@ from matplotlib import cm
 
 from statsmodels.tsa.stattools import adfuller
 from statsmodels.formula.api import mixedlm
+from h5io import read_hdf5, write_hdf5
 
 from autoreject import AutoReject
 

Preprocessing.py

@@ -25,7 +25,7 @@ Link to the demonstration data: www.bci2000.org
 
 # Set working directory
 import os
-wkdir = "/Users/benj3542/Desktop/Uni/Noter/Semester_6/Bachelor/resting-state-eeg-analysis/"
+wkdir = "/home/s200431/"
 os.chdir(wkdir)
 
 # Load all libraries from the Preamble
@@ -36,10 +36,27 @@ from mne.datasets import eegbci
 # EEG recordings from 2 subjects are used as an example
 # The EEGBCI200 is task based, but we will treat it as "resting-state"
 # And the 2 runs as Eyes Closed and Eyes Open
-n_subjects = 2
-Subject_id = [1,2]
+
+n_subjects = 91
+Subject_id = list(range(52,n_subjects+52))
 # Download and get filenames
 
+data_path = "/data/may2020/QEEG"
+files = []
+for r, d, f in os.walk(data_path):
+    for file in f: 
+        files.append(os.path.join(r,file))
+        
+files = [files[i:i+2] for i in range(0, len(files), 2)]
+
+
+"""
+n_subjects = 51
+Subject_id = list(range(1,n_subjects+1)) 
+# Original code to get filenames in a folder
+data_path = "/data/raw/FOR_DTU/rawEEGforDTU"
+
+
 from mne.datasets import eegbci
 files = []
 for i in range(n_subjects):
@@ -47,18 +64,26 @@ for i in range(n_subjects):
     files.append(raw_fnames)
 """
 # Original code to get filenames in a folder
-data_path = "/Users/benj3542/Desktop/Uni/Noter/Semester_6/Bachelor/Test_bdf"
+data_path = "/data/sep2020"
+"""
 # Get filenames
 holder = []
 files = []
 for r, d, f in os.walk(data_path):
     for file in f:
-        if ".bdf" in file:
-            holder.append((os.path.join(r, file)))
+        if ".set" in file:
+            holder.append(os.path.join(r, file))
     files.append(holder)
     holder = []
 files.pop(0)
+      
 """
+files = []
+for r, d, f in os.walk(data_path): 
+    for file in f: 
+        if ".set" in file: 
+            files.append(os.path.join(r, file))
+    
 # Eye status
 anno_to_event = {'Eyes Closed': 1, 'Eyes Open': 2} # manually defined event id
 eye_status = list(anno_to_event.keys())
@@ -70,15 +95,16 @@ n_epochs_trial = int(60/epoch_len) # number of epochs in a trial
 n_trials = 2 # 1 eyes closed followed by 1 eyes open
 
 # Montage settings
-montage = mne.channels.make_standard_montage('standard_1005')
-#montage = mne.channels.read_custom_montage(filename) # custom montage file
-
+fname = "/home/glia/Analysis/Channel_locations/SMARTING_fixed.loc"
+#montage = mne.channels.make_standard_montage('standard_1005')
+montage = mne.channels.read_custom_montage(fname) # custom montage file
+#mne.io.read_raw_eeglab(files[0][1], preload = True)
 # %% Load, filter and epoch (Steps 1 to 5)
 # Pre-allocate memory
 epochs = [0]*n_subjects
 for i in range(n_subjects):
     # MNE python supports many EEG formats. Make sure to use the proper one
-    raw = mne.io.concatenate_raws([mne.io.read_raw_edf(f, preload=True) for f in files[i]])
+    raw = mne.io.read_raw_eeglab(files[i],preload = True, verbose = 0)
     # Fix EEGBCI channel names
     eegbci.standardize(raw)
     # Set montage
@@ -256,10 +282,13 @@ artifacts[1] = [0, 2] # eye blinks, eye movement
 corrected_epochs = cleaned_epochs.copy()
 
 for n in range(len(cleaned_epochs)):
+    try: 
         # Define the components with artifacts    
         ica[n].exclude = artifacts[n]
         # Remove on corrected data
         ica[n].apply(corrected_epochs[n].load_data())
+    except: 
+        print("An error occurred")
 
 # Inspect how the ICA worked
 n=0; corrected_epochs[n].plot(scalings=200e-6, n_epochs = 10)