def runica(x, fs, channels, mode='ica'):
from PyQt5.QtWidgets import QApplication
from pynfb.protocols.ssd.topomap_selector_ica import ICADialog
a = QApplication([])
ica = ICADialog(x, channels, fs, mode=mode)
ica.exec_()
a.exit()
return ica.spatial, ica.topography
def runica(x, fs, channels, mode='ica'):
from PyQt5.QtWidgets import QApplication
from pynfb.protocols.ssd.topomap_selector_ica import ICADialog
a = QApplication([])
ica = ICADialog(x, channels, fs, mode=mode)
ica.exec_()
a.exit()
return ica.spatial, ica.topography
def run_ica_and_select_band(data, channels, fs):
app = QApplication([])
ica = ICADialog(data, channels, fs)
ica.exec_()
band = select_band(data.dot(ica.spatial), fs)
results = {
'spatial_filter': ica.spatial,
'spatial_filter_ind': ica.table.get_checked_rows()[0],
'filters': ica.decomposition.filters,
'topographies': ica.decomposition.topographies,
'band': band
}
return results
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
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
def get_some_data(real=False):
if not real:
channels = [
'Fp1', 'Fp2', 'F7', 'F3', 'Fz', 'F4', 'F8', 'Ft9', 'Fc5',
'Fc1', 'Fc2', 'Fc6', 'Ft10', 'T7', 'C3', 'Cz', 'C4', 'T8',
'Tp9', 'Cp5', 'Cp1', 'Cp2', 'Cp6', 'Tp10', 'P7', 'P3', 'Pz',
'P4', 'P8', 'O1', 'Oz', 'O2'
]
data = np.random.normal(loc=0,
scale=0.00001,
size=(5000, len(channels))).T
fs = 500
else:
import h5py
from pynfb.postprocessing.utils import get_info
with h5py.File(
r'D:\mu_ica\mu_ica\mu_ica_S1_D3_04-21_18-16-03\experiment_data.h5'
) as f:
fs, channels, p_names = get_info(f, [])
data = f['protocol{}/raw_data'.format(
p_names.index('Baseline') + 1)][:].T
from PyQt4.QtGui import QApplication
a = QApplication([])
rej, spatial, top = ICADialog.get_rejection(data.T,
channels,
fs,
mode='ica',
states=None)[:3]
data = rej.apply(data.T).T
return data, fs, channels
def get_some_data(real=False):
if not real:
channels = ['Fp1', 'Fp2', 'F7', 'F3', 'Fz', 'F4', 'F8', 'Ft9', 'Fc5', 'Fc1', 'Fc2', 'Fc6', 'Ft10', 'T7', 'C3', 'Cz',
'C4', 'T8', 'Tp9', 'Cp5', 'Cp1', 'Cp2', 'Cp6', 'Tp10', 'P7', 'P3', 'Pz', 'P4', 'P8', 'O1', 'Oz', 'O2']
data = np.random.normal(loc=0, scale=0.00001, size=(5000, len(channels))).T
fs = 500
else:
import h5py
from pynfb.postprocessing.utils import get_info
with h5py.File(r'D:\mu_ica\mu_ica\mu_ica_S1_D3_04-21_18-16-03\experiment_data.h5') as f:
fs, channels, p_names = get_info(f, [])
data = f['protocol{}/raw_data'.format(p_names.index('Baseline') + 1)][:].T
from PyQt5.QtWidgets import QApplication
a = QApplication([])
rej, spatial, top = ICADialog.get_rejection(data.T, channels, fs, mode='ica', states=None)[:3]
data = rej.apply(data.T).T
return data, fs, channels
#data[channels] = fft_filter(data[channels], fs, (1, 45))
#data = data.iloc[~get_outliers_mask(data[channels], std=3, iter_numb=2)]
#plt.plot(data[channels] + 1000*np.arange(len(channels)))
#plt.show()
print('ww', sum(get_outliers_mask(data[channels][data['block_number']<3])))
# spatial filter (SMR detection)
try:
filt = np.load('ica_{}.npy'.format(experiment))
#filt = np.zeros(len(channels))
#filt[channels.index('C3')] = 1
except FileNotFoundError:
a = QApplication([])
(rej, filt, topo, _unmix, _bandpass, _) = ICADialog.get_rejection(
data[channels][data['block_number'].isin([1, 2])]#.iloc[~get_outliers_mask(data[channels][data['block_number']<12], std=2)]
, channels, fs, mode='csp')
#(_rej, filt, topo, _unmix, _bandpass, _) = ICADialog.get_rejection(np.concatenate(list(x[y=='Left']) + l1ist(x[y=='Legs'])), channels, fs, mode='csp')
# filt, topography, bandpass, rejections = SelectSSDFilterWidget.select_filter_and_bandpass(np.concatenate(x), ch_names_to_2d_pos(channels), channels, sampling_freq=fs)
np.save('ica_{}.npy'.format(experiment), filt)
data['SMR'] = np.dot(data[channels], filt)
#data = data.iloc[~get_outliers_mask(data[['C3', 'SMR']], std=3, iter_numb=10)]
# temporal filter
data['SMR_band_filt'] = fft_filter(data['SMR'], fs, (11, 13))
data['SMR_env'] = np.abs(signal.hilbert(data['SMR_band_filt']))
data.to_pickle('data-{}.pkl'.format(experiment))
data['SMR'].plot()
data['SMR_band_filt'].plot()
tops = []
spats = []
fig1, axes = plt.subplots(nrows=2,
ncols=2,
sharex=True,
sharey=False)
for j, data in enumerate([first, last]):
raw_data = np.concatenate([
np.concatenate(
[raw[key] for key in data if 'Close' in key]),
np.concatenate([raw[key] for key in data if 'Open' in key])
])
rej, spatial, top = ICADialog.get_rejection(raw_data,
channels,
fs,
mode='csp',
states=None)[:3]
tops.append(top)
spats.append(spatial)
plot_topomap(top,
ch_names_to_2d_pos(channels),
axes=axes[j, 0],
show=False)
plot_topomap(spatial,
ch_names_to_2d_pos(channels),
axes=axes[j, 1],
show=False)
axes[j, 0].set_xlabel(
'Topography ({})'.format('before' if j == 0 else 'after'))
axes[j, 1].set_xlabel('Spatial filter ({})'.format(
from mne.io import read_epochs_eeglab
import numpy as np
import pylab as plt
from scipy.io import loadmat
from pynfb.widgets.helpers import ch_names_to_2d_pos
from pynfb.protocols.ssd.topomap_selector_ica import ICADialog
from PyQt5.QtWidgets import QApplication
fs = 258
pre = loadmat('C:\\Users\\nsmetanin\\Downloads\\nfb_bci\\wetransfer-07cfaf\\Subj01_POSTdata.mat')
channels = [a[0] for a in pre['EEGchanslabels'][0]]
pre = pre['EEGPOSTdata']
post = loadmat('C:\\Users\\nsmetanin\\Downloads\\nfb_bci\\wetransfer-07cfaf\\Subj01_PREdata.mat')['EEGPREdata']
print(pre.shape, post.shape, channels)
#print(ch_names_to_2d_pos(channels))
a = QApplication([])
n_samples = 50
print(pre[:, -n_samples:, 0].shape)
x = np.concatenate([np.concatenate([pre[:, -n_samples:, k].T for k in range(pre.shape[2])]),
np.concatenate([post[:, :n_samples, k].T for k in range(post.shape[2])])])
print(x.shape)
ICADialog.get_rejection(x, channels, fs, mode='csp')
from pynfb.signal_processing.helpers import get_outliers_mask
import numpy as np
from pynfb.protocols.ssd.topomap_selector_ica import ICADialog
from PyQt5 import QtGui, QtWidgets
fs = 1000
band = (8, 14)
channels = ['Fp1', 'Fp2', 'F7', 'F3', 'Fz', 'F4', 'F8', 'Ft9', 'Fc5', 'Fc1', 'Fc2', 'Fc6', 'Ft10', 'T3', 'C3', 'Cz',
'C4', 'T4', 'Tp9', 'Cp5', 'Cp1', 'Cp2', 'Cp6', 'Tp10', 'T5', 'P3', 'Pz', 'P4', 'T6', 'O1', 'Oz', 'O2']
data = [loadmat(r'C:\Users\Nikolai\Desktop\Liza_diplom_data\Liza_diplom_data\treatment\p25\day2\proba{}.mat'.format(k))['X1Topo'].T for k in range(1, 16)]
df = pd.DataFrame(data=np.concatenate(data), columns=channels)
for ch in['Pz', 'Cz']:
channels.remove(ch)
del df[ch]
df = df.loc[~get_outliers_mask(df[channels])]
#plt.plot(*welch(df['C3'], fs, nperseg=4*fs))
plt.plot(df[channels])
plt.show()
#plt.show()
#df['C3env'] = np.abs(hilbert(fft_filter(df['C3'], fs, band)))
#df['C4env'] = np.abs(hilbert(fft_filter(df['C4'], fs, band)))
#plt.plot(df['C3env']+df['C4env'])
a = QtWidgets.QApplication([])
(rej, filt, topo, _unmix, _bandpass, _) = ICADialog.get_rejection(df.iloc[:fs*60*3], channels, fs)
df['SMR'] = np.dot(df.as_matrix(), filt)
df.to_pickle('p4')
plt.plot(df['SMR'])
plt.show()
# return vals, vecs and topographics (in descending order)
reversed_slice = slice(-1, None, -1)
topo = inv(vecs[:,reversed_slice]).T
return vals[reversed_slice], vecs[:, reversed_slice], topo
if __name__ == '__main__':
dir_ = 'D:\\vnd_spbu\\pilot\\mu5days'
experiment = 'pilot5days_Rakhmankulov_Day3_03-01_12-51-41'
with h5py.File('{}\\{}\\{}'.format(dir_, experiment, 'experiment_data.h5')) as f:
ica = f['protocol1/signals_stats/left/rejections/rejection1'][:]
x_filters = dc_blocker(np.dot(f['protocol1/raw_data'][:], ica))
x_rotation = dc_blocker(np.dot(f['protocol2/raw_data'][:], ica))
x_dict = {
'closed': x_filters[:x_filters.shape[0] // 2],
'opened': x_filters[x_filters.shape[0] // 2:],
'rotate': x_rotation
}
drop_channels = ['AUX', 'A1', 'A2']
labels, fs = get_lsl_info_from_xml(f['stream_info.xml'][0])
print('fs: {}\nall labels {}: {}'.format(fs, len(labels), labels))
channels = [label for label in labels if label not in drop_channels]
scores, unmixing_matrix, topographies = csp3(x_dict, 250, (9, 14), lambda_=0.5, regularization_coef=0.01)
app = QtWidgets.QApplication([])
selector = ICADialog(np.vstack((x_filters, x_rotation)), channels, fs, unmixing_matrix=unmixing_matrix, mode='csp', scores=scores)
selector.exec_()
fs, channels, p_names = get_info(f, settings['drop_channels'])
rejection, alpha, ica = load_rejections(f, reject_alpha=False)
raw_before = OrderedDict()
raw_after = OrderedDict()
for j, name in enumerate(p_names):
if j < 3:
x = preproc(f['protocol{}/raw_data'.format(j + 1)][:], fs, rejection if reject else None)
raw_before = add_data_simple(raw_before, name, x)
elif j > len(p_names) - 3:
x = preproc(f['protocol{}/raw_data'.format(j + 1)][:], fs, rejection if reject else None)
print(x.shape)
raw_after = add_data_simple(raw_after, name, x)
xx = np.concatenate([raw_before['Opened'], raw_before['Baseline'], raw_after['Baseline'], raw_before['Left'], raw_before['Right'], raw_after['Left'], raw_after['Right'], ])
#xx[:, channels.index('C3')] = xx[:, channels.index('C3')]
rejection, spatial, topography, unmixing_matrix, bandpass, _ = ICADialog.get_rejection(xx, channels, fs, mode='csp', states=None)
from mne.viz import plot_topomap
print(spatial)
fig, axes = plt.subplots(ncols=rejection.topographies.shape[1])
if not isinstance(axes, type(axes1)) :
axes = [axes]
for ax, top in zip(axes, rejection.topographies.T):
plot_topomap(top, ch_names_to_2d_pos(channels), axes=ax, show=False)
fig.savefig('csp_S{}_D{}.png'.format(subj, day+1))
fig.show()
good_samples_mask = th < threshold
print('********Remove {} bad samples'.format(sum(~good_samples_mask)))
return df.loc[good_samples_mask].values, good_samples_mask
N_EEG_CHANNELS = 30
raw = mne.io.read_raw_fif('/media/kolai/prispasobo/Kondrashin_raw.fif')
channels = raw.info['ch_names'][:N_EEG_CHANNELS]
fs = int(raw.info['sfreq'])
data = raw.load_data().notch_filter([50, 100]).filter(0.5, 40.).get_data()[:N_EEG_CHANNELS].T
# plt.plot(data[:fs*100])
ica_data, good_samples_mask = remove_bad_samples(data[:fs*60*10], window_size=fs*5, threshold=2000)
ica = ICADialog(ica_data, channels, fs)
ica.exec_()
# ica.table.get_checked_rows()
plt.plot(data[:, :]+np.arange(30)*2000, 'r')
plt.plot(ica.rejection.apply(data)[:, :]+np.arange(30)*2000, 'k')
plt.fill_between(np.arange(data.shape[0]), (~good_samples_mask).astype(int)*30*2000, color='r', alpha=0.4)
plt.gca().set_yticks(np.arange(30)*2000)
plt.gca().set_yticklabels(channels)
plt.xlim(0, fs*30)
data = np.random.normal(loc=0, scale=0.00001, size=(5000, len(channels))).T
fs = 500
else:
import h5py
from pynfb.postprocessing.utils import get_info
with h5py.File(
r'D:\mu_ica\mu_ica\mu_ica_S1_D3_04-21_18-16-03\experiment_data.h5'
) as f:
fs, channels, p_names = get_info(f, [])
data = f['protocol{}/raw_data'.format(p_names.index('Baseline') +
1)][:].T
from PyQt4.QtGui import QApplication
a = QApplication([])
rej, spatial, top = ICADialog.get_rejection(data.T,
channels,
fs,
mode='ica',
states=None)[:3]
data = rej.apply(data.T).T
# create info
info = mne.create_info(ch_names=channels,
sfreq=fs,
montage=mne.channels.read_montage(kind='standard_1005'),
ch_types=['eeg' for ch in channels])
# raw instance
raw = mne.io.RawArray(data, info)
#raw.set_eeg_reference()
#noise_cov = mne.compute_raw_covariance(raw)
noise_cov = mne.make_ad_hoc_cov(info, verbose=None)
return df.loc[good_samples_mask].values, good_samples_mask
N_EEG_CHANNELS = 30
raw = mne.io.read_raw_fif('/media/kolai/prispasobo/Kondrashin_raw.fif')
channels = raw.info['ch_names'][:N_EEG_CHANNELS]
fs = int(raw.info['sfreq'])
data = raw.load_data().notch_filter([50, 100]).filter(
0.5, 40.).get_data()[:N_EEG_CHANNELS].T
# plt.plot(data[:fs*100])
ica_data, good_samples_mask = remove_bad_samples(data[:fs * 60 * 10],
window_size=fs * 5,
threshold=2000)
ica = ICADialog(ica_data, channels, fs)
ica.exec_()
# ica.table.get_checked_rows()
plt.plot(data[:, :] + np.arange(30) * 2000, 'r')
plt.plot(ica.rejection.apply(data)[:, :] + np.arange(30) * 2000, 'k')
plt.fill_between(np.arange(data.shape[0]),
(~good_samples_mask).astype(int) * 30 * 2000,
color='r',
alpha=0.4)
plt.gca().set_yticks(np.arange(30) * 2000)
plt.gca().set_yticklabels(channels)
plt.xlim(0, fs * 30)
channels = [
'Fp1', 'Fp2', 'F7', 'F3', 'Fz', 'F4', 'F8', 'Ft9', 'Fc5', 'Fc1', 'Fc2',
'Fc6', 'Ft10', 'T3', 'C3', 'Cz', 'C4', 'T4', 'Tp9', 'Cp5', 'Cp1', 'Cp2',
'Cp6', 'Tp10', 'T5', 'P3', 'Pz', 'P4', 'T6', 'O1', 'Oz', 'O2'
]
data = [
loadmat(
r'C:\Users\Nikolai\Desktop\Liza_diplom_data\Liza_diplom_data\treatment\p25\day2\proba{}.mat'
.format(k))['X1Topo'].T for k in range(1, 16)
]
df = pd.DataFrame(data=np.concatenate(data), columns=channels)
for ch in ['Pz', 'Cz']:
channels.remove(ch)
del df[ch]
df = df.loc[~get_outliers_mask(df[channels])]
#plt.plot(*welch(df['C3'], fs, nperseg=4*fs))
plt.plot(df[channels])
plt.show()
#plt.show()
#df['C3env'] = np.abs(hilbert(fft_filter(df['C3'], fs, band)))
#df['C4env'] = np.abs(hilbert(fft_filter(df['C4'], fs, band)))
#plt.plot(df['C3env']+df['C4env'])
a = QtGui.QApplication([])
(rej, filt, topo, _unmix, _bandpass,
_) = ICADialog.get_rejection(df.iloc[:fs * 60 * 3], channels, fs)
df['SMR'] = np.dot(df.as_matrix(), filt)
df.to_pickle('p4')
plt.plot(df['SMR'])
plt.show()
def run_ssd(self, row=None, csp=False, ica=False):
if row is None and ica:
row = self.table.ica_buttons.index(self.sender())
elif row is None and not csp:
row = self.table.buttons.index(self.sender())
elif row is None and csp:
row = self.table.csp_buttons.index(self.sender())
ind = self.get_checked_protocols()
print(ind)
x = concatenate([self.x[j] for j in ind[0]])
if csp:
x = concatenate([x] + [self.x[j] for j in ind[1]])
x = dot(x, self.signals[row].rejections.get_prod())
x = x[:, self.channels_mask]
to_all = False
ica_rejection = None
topography = None
if ica:
reply = QtGui.QMessageBox.Yes
if len(self.signals[row].rejections) > 0:
reply = QtGui.QMessageBox.question(
self, 'Warning', 'Changing ICA base selection will '
'invalidate the current rejections (CSP, SSD). '
'Are you sure you want to continue?',
QtGui.QMessageBox.Yes, QtGui.QMessageBox.No)
if reply == QtGui.QMessageBox.Yes:
result = ICADialog.get_rejection(
x,
self.channels_names,
self.sampling_freq,
decomposition=self.ica_unmixing_matrix)
ica_rejection, filter, topography, self.ica_unmixing_matrix, bandpass, to_all = result
rejections = []
elif csp:
rejection, filter, topography, _, bandpass, to_all = ICADialog.get_rejection(
x,
self.channels_names,
self.sampling_freq,
mode='csp',
_stimulus_split=self.stimulus_split.isChecked(),
marks=self.marks)
rejections = [rejection] if rejection is not None else []
else:
filter, topography, bandpass, rejections = SelectSSDFilterWidget.select_filter_and_bandpass(
x,
self.pos,
self.channels_names,
sampling_freq=self.sampling_freq)
if filter is not None:
filter_copy = np.zeros(len(self.channels_mask))
filter_copy[self.channels_mask] = filter
filter = filter_copy
if topography is not None:
topography_copy = np.zeros(len(self.channels_mask))
topography_copy[self.channels_mask] = topography
topography = topography_copy
if ica_rejection is not None:
ica_rejection = ica_rejection.expand_by_mask(self.channels_mask)
rejections = [
rej.expand_by_mask(self.channels_mask) for rej in rejections
]
rows = range(len(self.signals)) if to_all else [row]
print(to_all, rows)
for row_ in rows:
if ica_rejection is not None:
self.signals[row_].update_ica_rejection(ica_rejection)
if filter is not None:
self.signals[row_].update_spatial_filter(filter,
topography=topography)
if bandpass is not None:
self.signals[row_].update_bandpass(bandpass)
self.signals[row_].update_rejections(rejections, append=True)
modified_flag = len(
rejections) > 0 or bandpass is not None or filter is not None
self.table.update_row(row_, modified=modified_flag)
def run_ssd(self, row=None, csp=False, ica=False):
if row is None and ica:
row = self.table.ica_buttons.index(self.sender())
elif row is None and not csp:
row = self.table.buttons.index(self.sender())
elif row is None and csp:
row = self.table.csp_buttons.index(self.sender())
ind = self.get_checked_protocols()
print(ind)
x = concatenate([self.x[j] for j in ind[0]])
if csp:
x = concatenate([x] + [self.x[j] for j in ind[1]])
x = dot(x, self.signals[row].rejections.get_prod())
x = x[:, self.channels_mask]
to_all = False
ica_rejection = None
topography = None
if ica:
reply = QtWidgets.QMessageBox.Yes
if len(self.signals[row].rejections) > 0:
reply = QtWidgets.QMessageBox.question(self, 'Warning',
'Changing ICA base selection will '
'invalidate the current rejections (CSP, SSD). '
'Are you sure you want to continue?',
QtWidgets.QMessageBox.Yes, QtWidgets.QMessageBox.No)
if reply == QtWidgets.QMessageBox.Yes:
result = ICADialog.get_rejection(x, self.channels_names, self.sampling_freq,
decomposition=self.ica_unmixing_matrix)
ica_rejection, filter, topography, self.ica_unmixing_matrix, bandpass, to_all = result
rejections = []
elif csp:
rejection, filter, topography, _, bandpass, to_all = ICADialog.get_rejection(x, self.channels_names, self.sampling_freq,
mode='csp', _stimulus_split=self.stimulus_split.isChecked(),
marks=self.marks)
rejections = [rejection] if rejection is not None else []
else:
filter, topography, bandpass, rejections = SelectSSDFilterWidget.select_filter_and_bandpass(x, self.pos,
self.channels_names,
sampling_freq=
self.sampling_freq)
if filter is not None:
filter_copy = np.zeros(len(self.channels_mask))
filter_copy[self.channels_mask] = filter
filter = filter_copy
if topography is not None:
topography_copy = np.zeros(len(self.channels_mask))
topography_copy[self.channels_mask] = topography
topography = topography_copy
if ica_rejection is not None:
ica_rejection = ica_rejection.expand_by_mask(self.channels_mask)
rejections = [rej.expand_by_mask(self.channels_mask) for rej in rejections]
rows = range(len(self.signals)) if to_all else [row]
print(to_all, rows)
for row_ in rows:
if ica_rejection is not None:
self.signals[row_].update_ica_rejection(ica_rejection)
if filter is not None:
self.signals[row_].update_spatial_filter(filter, topography=topography)
if bandpass is not None:
self.signals[row_].update_bandpass(bandpass)
self.signals[row_].update_rejections(rejections, append=True)
modified_flag = len(rejections)>0 or bandpass is not None or filter is not None
self.table.update_row(row_, modified=modified_flag)
#data[channels] = fft_filter(data[channels], fs, (1, 45))
#data = data.iloc[~get_outliers_mask(data[channels], std=3, iter_numb=2)]
#plt.plot(data[channels] + 1000*np.arange(len(channels)))
#plt.show()
print('ww', sum(get_outliers_mask(data[channels][data['block_number']<3])))
# spatial filter (SMR detection)
try:
filt = np.load('ica_{}.npy'.format(experiment))
#filt = np.zeros(len(channels))
#filt[channels.index('C3')] = 1
except FileNotFoundError:
a = QApplication([])
(rej, filt, topo, _unmix, _bandpass, _) = ICADialog.get_rejection(
data[channels][data['block_number'].isin([1, 2])]#.iloc[~get_outliers_mask(data[channels][data['block_number']<12], std=2)]
, channels, fs, mode='csp')
#(_rej, filt, topo, _unmix, _bandpass, _) = ICADialog.get_rejection(np.concatenate(list(x[y=='Left']) + l1ist(x[y=='Legs'])), channels, fs, mode='csp')
# filt, topography, bandpass, rejections = SelectSSDFilterWidget.select_filter_and_bandpass(np.concatenate(x), ch_names_to_2d_pos(channels), channels, sampling_freq=fs)
np.save('ica_{}.npy'.format(experiment), filt)
data['SMR'] = np.dot(data[channels], filt)
#data = data.iloc[~get_outliers_mask(data[['C3', 'SMR']], std=3, iter_numb=10)]
# temporal filter
data['SMR_band_filt'] = fft_filter(data['SMR'], fs, (11, 13))
data['SMR_env'] = np.abs(signal.hilbert(data['SMR_band_filt']))
data.to_pickle('data-{}.pkl'.format(experiment))
data['SMR'].plot()
data['SMR_band_filt'].plot()
dir_ = 'D:\\vnd_spbu\\pilot\\mu5days'
experiment = 'pilot5days_Rakhmankulov_Day3_03-01_12-51-41'
with h5py.File('{}\\{}\\{}'.format(dir_, experiment,
'experiment_data.h5')) as f:
ica = f['protocol1/signals_stats/left/rejections/rejection1'][:]
x_filters = dc_blocker(np.dot(f['protocol1/raw_data'][:], ica))
x_rotation = dc_blocker(np.dot(f['protocol2/raw_data'][:], ica))
x_dict = {
'closed': x_filters[:x_filters.shape[0] // 2],
'opened': x_filters[x_filters.shape[0] // 2:],
'rotate': x_rotation
}
drop_channels = ['AUX', 'A1', 'A2']
labels, fs = get_lsl_info_from_xml(f['stream_info.xml'][0])
print('fs: {}\nall labels {}: {}'.format(fs, len(labels), labels))
channels = [label for label in labels if label not in drop_channels]
scores, unmixing_matrix, topographies = csp3(x_dict,
250, (9, 14),
lambda_=0.5,
regularization_coef=0.01)
app = QtWidgets.QApplication([])
selector = ICADialog(np.vstack((x_filters, x_rotation)),
channels,
fs,
unmixing_matrix=unmixing_matrix,
mode='csp',
scores=scores)
selector.exec_()
import pylab as plt
from scipy.io import loadmat
from pynfb.widgets.helpers import ch_names_to_2d_pos
from pynfb.protocols.ssd.topomap_selector_ica import ICADialog
from PyQt4.QtGui import QApplication
fs = 258
pre = loadmat(
'C:\\Users\\nsmetanin\\Downloads\\nfb_bci\\wetransfer-07cfaf\\Subj01_POSTdata.mat'
)
channels = [a[0] for a in pre['EEGchanslabels'][0]]
pre = pre['EEGPOSTdata']
post = loadmat(
'C:\\Users\\nsmetanin\\Downloads\\nfb_bci\\wetransfer-07cfaf\\Subj01_PREdata.mat'
)['EEGPREdata']
print(pre.shape, post.shape, channels)
#print(ch_names_to_2d_pos(channels))
a = QApplication([])
n_samples = 50
print(pre[:, -n_samples:, 0].shape)
x = np.concatenate([
np.concatenate([pre[:, -n_samples:, k].T for k in range(pre.shape[2])]),
np.concatenate([post[:, :n_samples, k].T for k in range(post.shape[2])])
])
print(x.shape)
ICADialog.get_rejection(x, channels, fs, mode='csp')
# data
real = False
if not real:
channels = ['Fp1', 'Fp2', 'F7', 'F3', 'Fz', 'F4', 'F8', 'Ft9', 'Fc5', 'Fc1', 'Fc2', 'Fc6', 'Ft10', 'T7', 'C3', 'Cz',
'C4', 'T8', 'Tp9', 'Cp5', 'Cp1', 'Cp2', 'Cp6', 'Tp10', 'P7', 'P3', 'Pz', 'P4', 'P8', 'O1', 'Oz', 'O2']
data = np.random.normal(loc=0, scale=0.00001, size=(5000, len(channels))).T
fs = 500
else:
import h5py
from pynfb.postprocessing.utils import get_info
with h5py.File(r'D:\mu_ica\mu_ica\mu_ica_S1_D3_04-21_18-16-03\experiment_data.h5') as f:
fs, channels, p_names = get_info(f, [])
data = f['protocol{}/raw_data'.format(p_names.index('Baseline') + 1)][:].T
from PyQt5.QtWidgets import QApplication
a = QApplication([])
rej, spatial, top = ICADialog.get_rejection(data.T, channels, fs, mode='ica', states=None)[:3]
data = rej.apply(data.T).T
standard_montage = mne.channels.read_montage(kind='standard_1005')
standard_montage_names = [name.upper() for name in standard_montage.ch_names]
for j, channel in enumerate(channels):
channels[j] = standard_montage.ch_names[standard_montage_names.index(channel.upper())]
# create info
info = mne.create_info(ch_names=channels, sfreq=fs, montage=mne.channels.read_montage(kind='standard_1005'), ch_types=['eeg' for ch in channels])
# raw instance
raw = mne.io.RawArray(data, info)
#raw.set_eeg_reference()
#noise_cov = mne.compute_raw_covariance(raw)
noise_cov = mne.make_ad_hoc_cov(info, verbose=None)
# forward solution
elif j > len(p_names) - 3:
x = preproc(f['protocol{}/raw_data'.format(j + 1)][:], fs,
rejection if reject else None)
print(x.shape)
raw_after = add_data_simple(raw_after, name, x)
xx = np.concatenate([
raw_before['Opened'],
raw_before['Baseline'],
raw_after['Baseline'],
raw_before['Left'],
raw_before['Right'],
raw_after['Left'],
raw_after['Right'],
])
#xx[:, channels.index('C3')] = xx[:, channels.index('C3')]
rejection, spatial, topography, unmixing_matrix, bandpass, _ = ICADialog.get_rejection(
xx, channels, fs, mode='csp', states=None)
from mne.viz import plot_topomap
print(spatial)
fig, axes = plt.subplots(ncols=rejection.topographies.shape[1])
if not isinstance(axes, type(axes1)):
axes = [axes]
for ax, top in zip(axes, rejection.topographies.T):
plot_topomap(top,
ch_names_to_2d_pos(channels),
axes=ax,
show=False)
fig.savefig('csp_S{}_D{}.png'.format(subj, day + 1))
fig.show()
if run_ica:
from PyQt5.QtWidgets import QApplication
ap = QApplication([])
all_keys = [key for key in raw.keys() if 'Left' in key or 'Right' in key or 'Close' in key or 'Open' in key]
first = all_keys[:len(all_keys)//2]
last = all_keys[len(all_keys)//2:]
tops = []
spats = []
fig1, axes = plt.subplots(nrows=2, ncols=2, sharex=True, sharey=False)
for j, data in enumerate([first, last]):
raw_data = np.concatenate([
np.concatenate([raw[key] for key in data if 'Close' in key]),
np.concatenate([raw[key] for key in data if 'Open' in key])
])
rej, spatial, top = ICADialog.get_rejection(raw_data, channels, fs, mode='csp', states=None)[:3]
tops.append(top)
spats.append(spatial)
plot_topomap(top, ch_names_to_2d_pos(channels), axes=axes[j, 0], show=False)
plot_topomap(spatial, ch_names_to_2d_pos(channels), axes=axes[j, 1], show=False)
axes[j, 0].set_xlabel('Topography ({})'.format('before' if j == 0 else 'after'))
axes[j, 1].set_xlabel('Spatial filter ({})'.format('before' if j == 0 else 'after'))
#plt.show()
# plot raw data
ch_plot = ['C3', 'P3', 'ICA']#, 'Pz', 'Fp1']
fig1, axes = plt.subplots(len(ch_plot), ncols=1, sharex=True, sharey=False)
print(axes)
