params = dict(Fs=4096.0, fpass=[5, 600], tapers=[1, 1], pad=1,
Npairs=2000, itc=1)
nPerDraw = 400
nDraws = 100
print 'Running Pairwise Spectrum Estimation'
(pS, f) = spectral.mtpspec(x, params, verbose='DEBUG')
print 'Running Raw Spectrum Estimation'
(Sraw, f) = spectral.mtspecraw(x, params, verbose=True)
print 'Running Mean Spectrum Estimation'
(S, N, f) = spectral.mtspec(x, params, verbose=True)
print 'Running CPCA PLV Estimation'
(cplv, f) = spectral.mtcpca(x, params, verbose=True)
# print 'Running CPCA Power Estimation'
# (cpow, f) = spectral.mtcspec(x, params, verbose=True)
# Saving Results
res = dict(pS=pS, cplv=cplv, Sraw=Sraw, f=f, S=S, N=N)
# res = dict(cpow = cpow, f = f)
save_name = subj + condstem + '.mat'
if (not os.path.isdir(respath)):
os.mkdir(respath)
if (not os.path.isdir(resonly_backup)):
os.mkdir(resonly_backup)
io.savemat(respath + save_name, res)
io.savemat(resonly_backup + save_name, res)
# Filter the data
raw.filter(l_freq = 70, h_freq = 1500, picks = np.arange(0,32,1))
# Here events 1 and 7 represent a particular stimulus in each polarity
selectedEve = dict(up0 = 1, down0 = 7)
# Epoching events of type 1 and 7
epochs = mne.Epochs(raw,eves,selectedEve,tmin = -0.05,
tmax = 0.45, baseline = (-0.05, 0), reject = dict(eeg=100e-6))
# Combining both polarities so I can get envelope related FFR responses
epochs = mne.epochs.combine_event_ids(epochs,['up0','down0'],dict(all0= 101))
# Getting the epoched data out, this step will also perform rejection
x = epochs.get_data()
# Reshaping to the format needed by spectral.mtcpca() and calling it
x = x.transpose((1,0,2))
x = x[0:32,:,:]
params = dict(Fs=4096,fpass=[5,1000],tapers=[2, 3],itc=1)
(plv,f) = spectral.mtcpca(x,params)
# Plotting results
pl.plot(f,plv,linewidth = 2)
pl.xlabel('Frequency (Hz)')
pl.grid(True)
pl.show()
# Here events 1 and 7 represent a particular stimulus in each polarity
selectedEve = dict(up0=1, down0=7)
# Epoching events of type 1 and 7
epochs = mne.Epochs(raw,
eves,
selectedEve,
tmin=-0.05,
tmax=0.45,
baseline=(-0.05, 0),
reject=dict(eeg=100e-6))
# Combining both polarities so I can get envelope related FFR responses
epochs = mne.epochs.combine_event_ids(epochs, ['up0', 'down0'], dict(all0=101))
# Getting the epoched data out, this step will also perform rejection
x = epochs.get_data()
# Reshaping to the format needed by spectral.mtcpca() and calling it
x = x.transpose((1, 0, 2))
x = x[0:32, :, :]
params = dict(Fs=4096, fpass=[5, 1000], tapers=[2, 3], itc=1)
(plv, f) = spectral.mtcpca(x, params)
# Plotting results
pl.plot(f, plv, linewidth=2)
pl.xlabel('Frequency (Hz)')
pl.grid(True)
pl.show()
pl.xlabel('Time (s)')
pl.ylabel('Frequency (Hz)')
pl.title('Phase Locking')
pl.colorbar()
pl.show()
# Induced power
pl.figure()
bmin, bmax = -0.1, 0.0
powbline = tfspec[:, np.logical_and(times < bmax,
times > bmin)].mean(axis=1)
pl.imshow((tfspec.T-powbline.T).T, vmin=-3.0, vmax=3.0,
extent=[times[0] - t0, times[-1] - t0, freqs[0], freqs[-1]],
aspect='auto', origin='lower')
pl.xlabel('Time (s)')
pl.ylabel('Frequency (Hz)')
pl.title('Power (dB re: baseline)')
pl.colorbar()
pl.show()
#########################################################################
# Fourier domain stuff
pl.figure()
params = dict(Fs=Fs, fpass=[5, 1000], tapers=[1, 1], itc=1)
y = x[:, :32, :].transpose((1, 0, 2))
plv, f = spectral.mtcpca(y, params, verbose='DEBUG')
pl.plot(f, plv**0.5, linewidth=2)
pl.xlabel('Frequency (Hz)', fontsize=16)
pl.ylabel('Intertrial PLV', fontsize=16)
pl.show()
#%% ITC
dataAll = epoch_data.get_data(picks=range(0, 32))
data32 = epoch_data.get_data(picks='A32').squeeze().T
t = epoch_data.times
t1 = np.where(t >= 0)[0][0]
t2 = np.where(t >= 1)[0][0]
data32 = data32[t1:t2, :]
fs = data_eeg.info['sfreq']
TW = 2 #fres = 2*TW
itc, f = ITC(data32, TW, fs)
plt.figure()
plt.plot(f, itc)
data_reshaped = np.zeros(
[dataAll.shape[1], dataAll.shape[0], dataAll.shape[2]]) #ch x trial x time
for ch in range(0, 32):
data_reshaped[ch, :, :] = dataAll[:, ch, :]
params = {}
params['Fs'] = fs
params['tapers'] = [TW, 2 * TW - 1]
params['fpass'] = [1, 100]
params['itc'] = 1
plv, f = mtcpca(data_reshaped, params)
plt.figure()
plt.plot(f, plv)
plt.xlabel('Freq (hz)')