Resolved merge conflict

jitter/ BCISpeller/BCISpellerV2.py

@@ -10,7 +10,8 @@ channels = ['Fp1', 'Fz', 'F3', 'F7', 'F9', 'FC5', 'FC1', 'C3', 'T7', 'CP5', 'CP1
             , 'P9', 'O1', 'Oz', 'O2', 'P10', 'P8', 'P4', 'CP2', 'CP6', 'T8', 'C4', 'Cz'
             , 'FC2', 'FC6', 'F10', 'F8', 'F4', 'Fp2', 'ACC_X', 'ACC_Y', 'ACC_Z']
 removed_channels = ['Fp1', 'F8', 'F7', 'Fp2', 'F3', 'F4']
-frequencies_main = [4,5,6,7,9,11]
+#frequencies_main = [4,5,6,7,9,11]
+frequencies_main = [4,5,5.5,6,7,7.4]
 frequencies = ['8.18_sin_h1','8.18_cos_h1','8.18_sin_h2','8.18_cos_h2','8.18_sin_h3','8.18_cos_h3',
                '9_sin_h1', '9_cos_h1','9_sin_h2', '9_cos_h2','9_sin_h3', '9_cos_h3',
                '10_sin_h1','10_cos_h1','10_sin_h2','10_cos_h2','10_sin_h3','10_cos_h3',
@@ -110,6 +111,7 @@ inlet_2 = StreamInlet(streams_eeg[0])
 
 fs = 250  # Sampling frequency
 delay = 0.061
+
 fragment_duration = 4+delay  # Fragment duration in seconds
 print(fragment_duration)
 fragment_samples = round(fs * fragment_duration)
@@ -176,6 +178,7 @@ while True:
                 X_c, Y_c = ca.transform(X, Y)
                 # Uses two coefficients pk = sqrt(p1**2+p2*'2)
                 p1 = np.corrcoef(X_c[:, 0], Y_c[:, 0])[0][1]
+                #freqs.append(p1)
                 p2 = np.corrcoef(X_c[:, 1], Y_c[:, 1])[0][1]
                 freqs.append(np.sqrt(p1 ** 2 + p2 ** 2))
             cca = freqs

jitter/ BCISpeller/BCISpellerV3.py

@@ -15,7 +15,8 @@ channels = ['Fp1', 'Fz', 'F3', 'F7', 'F9', 'FC5', 'FC1', 'C3', 'T7', 'CP5', 'CP1
 removed_channels = ['Fp1', 'F8', 'F7', 'Fp2', 'F3', 'F4']
 
 #The frequencies used for the SSVEP speller
-frequencies_main = [4,5,6,7,9,11]
+#frequencies_main = [4,5,6,7,9,11]
+frequencies_main = [4,5,5.5,6,7,7.4]
 
 #The channels used for the BCI Speller combined with CCA
 occ_channels = ['O1', 'O2', 'Oz', 'P3', 'P4', 'Pz', 'P7', 'P8']
@@ -190,6 +191,7 @@ inlet_2 = StreamInlet(streams_eeg[0])# LSL EEG data
 
 fs = 250  # Sampling frequency
 delay = 0.061 #Occular delay
+
 fragment_duration = 4+delay  # Fragment duration in seconds
 fragment_samples = round(fs * fragment_duration)
 
@@ -269,6 +271,7 @@ while True:
                 X_c, Y_c = ca.transform(X, Y)
                 # Uses two coefficients pk = sqrt(p1**2+p2*'2)
                 p1 = np.corrcoef(X_c[:, 0], Y_c[:, 0])[0][1]
+                #freqs.append(p1)
                 p2 = np.corrcoef(X_c[:, 1], Y_c[:, 1])[0][1]
                 freqs.append(np.sqrt(p1 ** 2 + p2 ** 2))
             cca = freqs