02_preprocessing.py

@@ -133,6 +133,7 @@ for i, p_row in p_list.iterrows():
 
         # interpolate bad channels (spherical spline method) to avoid biasing average reference
         print('Interpolating electrodes: ', raw.info['bads'])
+        n_chs_interpolated = len(raw.info['bads'])
         raw = raw.interpolate_bads(method={'eeg': 'spline'})
         # raw.plot()
 
@@ -261,6 +262,7 @@ for i, p_row in p_list.iterrows():
     subj_metadata['n_epochs'] = len(epochs_nt)
     subj_metadata['n_epochs_excluded'] = 750 - len(epochs_nt)
     subj_metadata['n_extra_chans_excluded'] = len(new_chs_to_interp)
+    subj_metadata['n_total_chans_interpolated'] = n_chs_interpolated
     subj_metadata['n_preprocessing_attempts'] = n_attempts
 
     # save epochs object

preprocessing_metadata.csv

@@ -0,0 +1,16 @@
+subj_id,n_false_neg_resp,n_false_pos_resp,n_ica_excluded,min_trials_per_char,mean_trials_per_char,max_trials_per_char,n_epochs,n_epochs_excluded,n_extra_chans_excluded,n_total_chans_interpolated,n_preprocessing_attempts
+sub-01,0,2,7,20,23.5,25,705,45,0,3,1
+sub-02,0,0,13,24,24.9,25,747,3,0,1,1
+sub-03,0,2,5,24,24.8,25,744,6,0,0,1
+sub-04,1,1,9,24,24.833333333333332,25,745,5,0,2,1
+sub-06,1,5,8,24,24.8,25,744,6,0,1,1
+sub-07,8,4,11,24,24.8,25,744,6,0,1,1
+sub-08,1,1,13,24,24.766666666666666,25,743,7,0,0,1
+sub-09,0,0,9,25,25.0,25,750,0,0,1,1
+sub-10,2,1,1,24,24.966666666666665,25,749,1,0,4,1
+sub-11,0,0,19,25,25.0,25,750,0,0,1,1
+sub-12,0,1,14,24,24.966666666666665,25,749,1,0,2,1
+sub-13,4,0,7,25,25.0,25,750,0,0,0,1
+sub-14,1,4,6,22,24.166666666666668,25,725,25,0,5,1
+sub-15,0,1,6,24,24.966666666666665,25,749,1,0,0,1
+sub-16,0,3,7,18,22.166666666666668,25,665,85,0,2,1