updated emcp to subtract average from each conditoin instead of overall

Author: kylemath

P3_exampleBV.py

@@ -23,7 +23,7 @@
 		raw = LoadBVData(sub,session,data_dir,exp)
 		#Pre-Process EEG Data
 		temp_epochs = PreProcess(raw,event_id,
-							emcp_epochs=True, rereference=True,
+							emcp_epochs=False, rereference=True,
 							plot_erp=False, rej_thresh_uV=1000, 
 							epoch_time=(-.2,1), baseline=(-.2,0) )
 		if len(temp_epochs) > 0:
@@ -32,13 +32,11 @@
 			print('Sub ' + sub + ', Cond ' 
 					+ session + 'all trials rejected')
 
-print(epochs)
 epochs = concatenate_epochs(epochs)	
-print(epochs)
 
 #Engineer Features for Model
 feats = FeatureEngineer(epochs,model_type='NN',electrode_median=False,
-						frequency_domain=False)
+ 						frequency_domain=False)
 #Create Model
 model,_ = CreateModel(feats, units=[16,16])
 #Train with validation, then Test

emcp_test.py

@@ -1,4 +1,10 @@
 from utils import *
+
+
+
+
+
+
 data_dir = '/Users/kylemathewson/Desktop/data/'
 exp = 'P3'
 subs = [ '001']
@@ -9,23 +15,21 @@
 sub = subs[0]
 session = sessions[0]
 raw = LoadBVData(sub,session,data_dir,exp)
+
+
 epochs = PreProcess(raw,event_id,
+				emcp_epochs=False, rereference=True,
+				plot_erp=True, rej_thresh_uV=250, 
+				epoch_time=(-.2,1), baseline=(-.2,0) )
+epochs_new = PreProcess(raw,event_id,
 				emcp_epochs=True, rereference=True,
-				plot_erp=False, rej_thresh_uV=250, 
+				plot_erp=True, rej_thresh_uV=250, 
 				epoch_time=(-.2,1), baseline=(-.2,0) )
 
+
 #plot results
 epochs['Target'].plot()
 epochs_new['Target'].plot()
 
 
 
-
-
-
-
-
-
-
-
-

utils.py

@@ -308,18 +308,31 @@ def GrattonEmcpEpochs(epochs):
   # -use coefficients to subtract eog from eeg
   '''
 
+  event_names = ['A_error','B_error']
+  i = 0
+  for key, value in sorted(epochs.event_id.items(), key=lambda x: (x[1], x[0])):
+    event_names[i] = key
+    i += 1
+
   #select the correct channels and data
   eeg_chans = pick_types(epochs.info, eeg=True, eog=False)
   eog_chans = pick_types(epochs.info, eeg=False, eog=True)
-  data = epochs._data
+  original_data = epochs._data
 
   #subtract the average over trials from each trial
-  avg = np.mean(epochs._data,axis=0)
-  rem = data-avg
+  rem = {}
+  for event in event_names:
+    data = epochs[event]._data
+    avg = np.mean(epochs[event]._data,axis=0)
+    rem[event] = data-avg
+
+  #concatenate trials together of different types
+  ## then put them all back together in X (regression on all at once)
+  allrem = np.concatenate([rem[event] for event in event_names])
 
   #separate eog and eeg
-  X = rem[:,eeg_chans,:]
-  Y = rem[:,eog_chans,:]
+  X = allrem[:,eeg_chans,:]
+  Y = allrem[:,eog_chans,:]
 
   #subtract mean over time from every trial/channel
   X = (X.T - np.mean(X,2).T).T
@@ -335,8 +348,8 @@ def GrattonEmcpEpochs(epochs):
   b = np.linalg.solve(np.dot(Y,Y.T), np.dot(Y,X.T))
 
   #get original data and electrodes first for matrix math
-  raw_eeg = np.moveaxis(data[:,eeg_chans,:],0,1)
-  raw_eog = np.moveaxis(data[:,eog_chans,:],0,1)
+  raw_eeg = np.moveaxis(original_data[:,eeg_chans,:],0,1)
+  raw_eog = np.moveaxis(original_data[:,eog_chans,:],0,1)
 
   #subtract weighted eye channels from eeg channels
   eeg_corrected = (raw_eeg.T - np.dot(raw_eog.T,b)).T