a = QApplication([])
ica = ICADialog(x, channels, fs, mode=mode)
ica.exec_()
a.exit()
return ica.spatial, ica.topography
def runica2(x, fs, channels, names=('Right', 'Left'), mode='ica'):
from PyQt5.QtWidgets import QApplication
from pynfb.protocols.ssd.topomap_selector_ica import ICADialog
a = QApplication([])
res = []
decomposition = None
for n in names:
print('*** Select component for condition: ' + n)
ica = ICADialog(x, channels, fs, decomposition=decomposition, mode=mode)
ica.exec_()
res.append(np.array((ica.spatial, ica.topography)))
decomposition = ica.decomposition
a.exit()
return res
if __name__ == '__main__':
from mne.viz import plot_topomap
from pynfb.inlets.montage import Montage
from pynfb.generators import ch_names32
montage = Montage(ch_names32)
spatial, topo = runica(np.random.normal(size=(100000, 32)), 1000, montage.get_names(), mode='csp')
plot_topomap(spatial, montage.get_pos())
plot_topomap(topo, montage.get_pos())
df['block_name'] = np.concatenate([[p]*len(d) for p, d in zip(p_names, data)])
df['block_number'] = np.concatenate([[j + 1]*len(d) for j, d in enumerate(data)])
df['alpha'] = np.dot(df[channels], spat)
df['alpha_env'] = df['alpha']*0
df['time'] = np.arange(len(df)) / fs
df['signal'] = np.concatenate(signal)
fig, axes = plt.subplots(1, 5)
eeg = fft_filter(df.loc[df['block_name'].isin(['Open', 'Close']), channels], fs, band)
topo = np.dot(np.dot(eeg.T, eeg), spat)
axes[2].set_xlabel('Spat. filt.')
plot_topomap(spat, montage.get_pos(), axes=axes[2], show=False)
axes[3].set_xlabel('Topography')
plot_topomap(topo, montage.get_pos(), axes=axes[3], show=False)
axes[0].plot(df.loc[df['block_number']==2, 'time'], fft_filter(df.loc[df['block_number']==2, 'alpha'], fs, (2, 100)))
axes[0].set_xlim(31, 32)
axes[0].set_xlabel('Time, s')
axes[0].set_ylabel('Voltage, $\mu V$')
#axes[2].plot(fft_filter(df.loc[df['block_number']==2, 'alpha'], fs, (1, 45)))
axes[1].plot(*welch(df.loc[df['block_name'].isin(['Close']), 'alpha'], fs, nperseg=fs*4))
axes[1].plot(*welch(df.loc[df['block_name'].isin(['Open']), 'alpha'], fs, nperseg=fs*4))
axes[1].legend(['Close', 'Open'])
axes[1].set_xlim(5, 15)
axes[1].set_xlabel('Freq., Hz')
axes[1].set_ylabel('PSD, $\mu V^2/Hz$')
a = QApplication([])
ica = ICADialog(x, channels, fs, mode=mode)
ica.exec_()
a.exit()
return ica.spatial, ica.topography
def runica2(x, fs, channels, names=('Right', 'Left'), mode='ica'):
from PyQt5.QtWidgets import QApplication
from pynfb.protocols.ssd.topomap_selector_ica import ICADialog
a = QApplication([])
res = []
decomposition = None
for n in names:
print('*** Select component for condition: ' + n)
ica = ICADialog(x, channels, fs, decomposition=decomposition, mode=mode)
ica.exec_()
res.append(np.array((ica.spatial, ica.topography)))
decomposition = ica.decomposition
a.exit()
return res
if __name__ == '__main__':
from mne.viz import plot_topomap
from pynfb.inlets.montage import Montage
from pynfb.generators import ch_names32
montage = Montage(ch_names32)
spatial, topo = runica(np.random.normal(size=(100000, 32)), 1000, montage.get_names(), mode='csp')
plot_topomap(spatial, montage.get_pos())
plot_topomap(topo, montage.get_pos())
'block_name=="{}"'.format(block_name))[channels],
fs,
nperseg=fs,
axis=0)
alpha_pow = pxx[(freq > 8) & (freq < 14)].mean(0)
alpha_pows.append(alpha_pow)
#plt.plot(freq, pxx, label=block_name)
#plt.plot(df.query('block_name=="{}"'.format(block_name))['C3'])
# plot_topomap(alpha_pow, montage.get_pos(), axes=axes[j], show=False)
# axes[j].set_title(block_name)
fig, axes = plt.subplots(1, 3)
ers_monitor = (alpha_pows[1] - alpha_pows[0]) / alpha_pows[0]
ers_vr = (alpha_pows[3] - alpha_pows[2]) / alpha_pows[2]
plot_topomap(ers_monitor,
montage.get_pos(),
axes=axes[0],
show=False,
vmin=-0.5,
vmax=0.5)
plot_topomap(ers_vr,
montage.get_pos(),
axes=axes[1],
show=False,
vmin=-0.5,
vmax=0.5)
plot_topomap(ers_vr - ers_monitor,
montage.get_pos(),
axes=axes[2],
show=False,
vmin=-0.5,
for day in [1, 2]:
mat = loadmat(
r'C:\Users\Nikolai\Desktop\Liza_diplom_data\Liza_diplom_data\treatment\{0}\bci\{0}_{1}1.mat'
.format(subj, day))
channels = [ch[0] for ch in mat['chan_names'][0]]
montage = Montage(channels)
df = pd.DataFrame(data=mat['data_cur'].T, columns=channels)
df['state'] = mat['states_cur'][0]
print(df['state'].unique())
df = df.loc[~get_outliers_mask(df[channels], iter_numb=10, std=3)]
plt.plot(df[channels])
plt.show()
a = QtGui.QApplication([])
#ica = ICADialog(np.concatenate([df.loc[df['state']==6, channels], df.loc[df['state']==1, channels]]), channels, fs, mode='csp')
ica = ICADialog(df[channels], channels, fs, mode='ica')
ica.exec_()
print(ica.table.get_checked_rows())
ind = ica.table.get_checked_rows()
for k in ind:
plot_topomap(ica.topographies[:, k], montage.get_pos())
np.save(r'treatment\{0}d{1}_filters.npy'.format(subj, day),
ica.unmixing_matrix)
np.save(r'treatment\{0}d{1}_topographies.npy'.format(subj, day),
ica.topographies)
np.save(r'treatment\{0}d{1}_smr_ind.npy'.format(subj, day), ind)
channels = [ch[0] for ch in mat['chan_names'][0]]
montage = Montage(channels)
df = pd.DataFrame(data=mat['data_cur'].T, columns=channels)
df['state'] = mat['states_cur'][0]
df = df.loc[~get_outliers_mask(df[channels], iter_numb=10, std=3)]
df['block_name'] = df['state'].apply(lambda x: {6: 'Rest', 1: 'Left', 2: 'Right'}[x])
filters = np.load(r'treatment\{0}d{1}_filters.npy'.format(subj, day+1))
topos = np.load(r'treatment\{0}d{1}_topographies.npy'.format(subj, day+1))
ind = np.load(r'treatment\{0}d{1}_smr_ind.npy'.format(subj, day+1))
for k in range(2):
spat = filters[:, ind[k]]
topo = topos[:, ind[k]]
df['smr'] = np.dot(df[channels], spat)
axes[day + 2*s, 0 + 3*k].set_xlabel('Spat. filt.')
plot_topomap(spat, montage.get_pos(), axes=axes[day + 2*s, 0+ 3*k], show=False, contours=0)
axes[day + 2*s, 1+ 3*k].set_xlabel('Topography')
plot_topomap(topo, montage.get_pos(), axes=axes[day + 2*s, 1+ 3*k], show=False, contours=0)
axes[day + 2*s, 2+ 3*k].plot(*welch(df.loc[df['block_name'].isin(['Rest']), 'smr']*1000000, fs, nperseg=fs*4))
axes[day + 2*s, 2+ 3*k].plot(*welch(df.loc[df['block_name'].isin(['Left']), 'smr']*1000000, fs, nperseg=fs*4))
axes[day + 2*s, 2+ 3*k].plot(*welch(df.loc[df['block_name'].isin(['Right']), 'smr']*1000000, fs, nperseg=fs*4))
if day == 0 and s == 0 and k ==1:
axes[day + 2*s, 2+ 3*k].legend(['Rest', 'Left', 'Right'])
axes[day + 2*s, 2+ 3*k].set_xlim(2, 30)
axes[day + 2*s, 2+ 3*k].set_xlabel('Freq., Hz')
axes[day + 2*s, 2+ 3*k].set_ylabel('PSD, $\mu V^2/Hz$')
plt.show()
csp = CSPDecomposition(montage.get_names(), FS,
subj_bands[dataset].tolist())
b_numbers = df.query('block_name=="FB"')['block_number'].unique()
x = np.concatenate([
df.query('block_number=={}'.format(b))[channels[:-1]].values
for b in b_numbers[1:]
])
y = np.concatenate([
np.zeros(sum(df['block_number'] == b)) + int(b < b_numbers[8])
for b in b_numbers[1:]
])
csp.fit(x, y)
#plt.figure()
plt.plot(x.dot(csp.filters[:, 1]))
plt.plot(df['P4'])
[
plot_topomap(csp.topographies[:, j],
montage.get_pos(),
axes=axes[j_dataset, j],
show=False,
contours=0,
mask=np.array(channels[:-1]) == 'P4') for j in range(32)
]
[
axes[j_dataset, j].set_title('{:.1f}'.format(csp.scores[j]))
for j in range(32)
]
axes[j_dataset, 0].set_ylabel('{}\n{:.1f}'.format(fb_type, snr))
channels = [ch[0] for ch in mat['chan_names'][0]]
montage = Montage(channels)
df = pd.DataFrame(data=mat['data_cur'].T, columns=channels)
print(channels)
print(1000*np.isin(np.array(channels), ['Fp1', 'Fp2', 'F7', 'F8', 'Ft9', 'Ft10', 'T7', 'T8', 'Tp9', 'Tp10']))
df['state'] = mat['states_cur'][0]
print(df['state'].unique())
df = df.loc[~get_outliers_mask(df[channels], iter_numb=10, std=3)]
plt.plot(df[channels])
plt.show()
ica = CSPDecomposition(channels, fs, band)
df = df.loc[df.state.isin([6, 1])]
scores, filters, topos = ica.decompose(df[channels].as_matrix(), df['state']-1)
for k in range(len(topos)):
plot_topomap(topos[:, k], montage.get_pos())
sources = fft_filter(np.dot(df[channels], filters), fs, band)
desyncs = sources[df.state == 6].std(0)/sources[df.state == 1].std(0)
smr_ind = np.argmax(desyncs)
df['SMR'+str(1)] = np.dot(df[channels], filters[:, smr_ind])
plot_topomap(filters[:, smr_ind], montage.get_pos())
plot_topomap(topos[:, smr_ind], montage.get_pos())
#df['SMR-env'+str(state)] = np.abs(hilbert(fft_filter(df[state+'SMR'], fs, [9, 13])))
plt.plot(df['SMR'+str(1)])
plt.plot(df['C3'])
plt.plot(df['C4'])
plt.show()
def ch_names_to_2d_pos(list_of_ch_names, kind='standard_1005', azimuthal=True):
montage = Montage(list_of_ch_names)
pos = montage.get_pos()
return pos
day = 1
for day in [1, 2]:
mat = loadmat(r'C:\Users\Nikolai\Desktop\Liza_diplom_data\Liza_diplom_data\treatment\{0}\bci\{0}_{1}1.mat'.format(subj, day))
channels = [ch[0] for ch in mat['chan_names'][0]]
montage = Montage(channels)
df = pd.DataFrame(data=mat['data_cur'].T, columns=channels)
df['state'] = mat['states_cur'][0]
print(df['state'].unique())
df = df.loc[~get_outliers_mask(df[channels], iter_numb=10, std=3)]
plt.plot(df[channels])
plt.show()
a = QtGui.QApplication([])
#ica = ICADialog(np.concatenate([df.loc[df['state']==6, channels], df.loc[df['state']==1, channels]]), channels, fs, mode='csp')
ica = ICADialog(df[channels], channels, fs, mode='ica')
ica.exec_()
print(ica.table.get_checked_rows())
ind = ica.table.get_checked_rows()
for k in ind:
plot_topomap(ica.topographies[:, k], montage.get_pos())
np.save(r'treatment\{0}d{1}_filters.npy'.format(subj, day), ica.unmixing_matrix)
np.save(r'treatment\{0}d{1}_topographies.npy'.format(subj, day), ica.topographies)
np.save(r'treatment\{0}d{1}_smr_ind.npy'.format(subj, day), ind)
filt = decomposition.filters[:, k]
go_data = X.loc[X.block_name == 'Go', eeg_channels].values
st_data = X.loc[X.block_name == 'Stay', eeg_channels].values
freq, go_spec = sg.welch(go_data.dot(filt), fs)
freq, st_spec = sg.welch(st_data.dot(filt), fs)
freq_slice = (freq > erd_band[0]) & (freq < erd_band[1])
erds[k] = (st_spec[freq_slice].mean() -
go_spec[freq_slice].mean()) / st_spec[freq_slice].mean()
# plot axes
for j, k in enumerate(np.argsort(erds)[::-1]):
topo = decomposition.topographies[:, k]
filt = decomposition.filters[:, k]
ax = axes[j // n_cols, j % n_cols * 2]
plot_topomap(topo, montage.get_pos(), axes=ax, show=False, contours=0)
ax.set_title(str(k))
ax.set_xlabel('{:.1f}%'.format(erds[k] * 100))
go_data = X.loc[X.block_name == 'Go', eeg_channels].values
st_data = X.loc[X.block_name == 'Stay', eeg_channels].values
freq, go_spec = sg.welch(go_data.dot(filt), fs)
freq, st_spec = sg.welch(st_data.dot(filt), fs)
freq_slice = (freq > 3) & (freq < 40)
ax = axes[j // n_cols, j % n_cols * 2 + 1]
ax.plot(freq[freq_slice], go_spec[freq_slice])
ax.plot(freq[freq_slice], st_spec[freq_slice])
ax.fill_between(freq[freq_slice],
go_spec[freq_slice],
st_spec[freq_slice],