def eegFeatures(video):
#EEG features
features = []
#alpha beta
alpha_total = np.sum(FE.powers(video, 'alpha'))
beta_total = np.sum(FE.powers(video, 'beta' ))
features.append(alpha_total / beta_total)
#l-R / L+R
left_alpha = 0
right_alpha = 0
for ch_left, ch_right in zip(all_left_channels, all_right_channels):
left = []
left.append( video[channelNames[ch_left],:] )
left_alpha += FE.powers(np.array(left) , 'alpha')[0]
right = []
right.append(video[channelNames[ch_right],:])
right_alpha += FE.powers(np.array(right), 'alpha')[0]
features.append( (left_alpha - right_alpha)/(left_alpha + right_alpha) )
#FM
frontal = []
frontal.append(video[channelNames['Fz']])
frontalMidlinePower = FE.powers( np.array(frontal),'theta')[0]
features.append(frontalMidlinePower)
#features look like this (person specific)
#list = | alpha/beta | L-R/L+R | FM |
return features
def featureFunc(samples):
features = []
features.extend(FE.powers(samples,'alpha'))
features.extend(FE.powers(samples,'delta'))
features.extend(FE.powers(samples,'gamma'))
features.extend(FE.powers(samples,'beta'))
#features.extend(FE.powers(samples,'alpha'))#todo lower and upper beta
return np.array(features)
