def load_data(self, filename=None, params=None, create_frame_seg=50):
self.dataframe = np.zeros((9, 288, 22, 1000))
self.datalabel = np.zeros((9, 288))
for i in range(1, 10, 1):
AT_slice = h5py.File('dataset/A0' + str(i) + 'T_slice.mat', 'r')
X = np.copy(AT_slice['image'])
X = X[:, :22, :] # select first 22 channels
y = np.copy(AT_slice['type'])
y = y[0, 0:X.shape[0]:1]
y = np.asarray(y, dtype=np.int32)
# replace NaN as 0
X[np.isnan(X)] = 0
self.datadict['A0' + str(i) + 'T'] = (X, y)
self.dataframe[i - 1, :, :, :] = X
self.datalabel[i - 1, :] = y
# preprocessing using braincode
for i in range(9):
for j in range(228):
self.dataframe[i, j] = np.transpose(
signalproc.highpass_cnt(np.transpose(self.dataframe[i, j]),
4,
1000,
filt_order=3,
axis=0))
self.dataframe[i, j] = np.transpose(
signalproc.exponential_running_standardize(
np.transpose(self.dataframe[i, j]),
factor_new=0.001,
init_block_size=None,
eps=0.0001))
print("Data filtered")
if create_frame_seg:
self.create_frame(create_frame_seg)
print("Data fully loaded!")
def load_bbci_data(filename, low_cut_hz):
load_sensor_names = None
loader = BBCIDataset(filename, load_sensor_names=load_sensor_names)
log.info("Loading data...")
cnt = loader.load()
# Cleaning: First find all trials that have absolute microvolt values
# larger than +- 800 inside them and remember them for removal later
log.info("Cutting trials...")
marker_def = OrderedDict([('Right Hand', [1]), ('Left Hand', [2],),
('Rest', [3]), ('Feet', [4])])
clean_ival = [0, 4000]
set_for_cleaning = create_signal_target_from_raw_mne(cnt, marker_def,
clean_ival)
clean_trial_mask = np.max(np.abs(set_for_cleaning.X), axis=(1, 2)) < 800
log.info("Clean trials: {:3d} of {:3d} ({:5.1f}%)".format(
np.sum(clean_trial_mask),
len(set_for_cleaning.X),
np.mean(clean_trial_mask) * 100))
# now pick only sensors with C in their name
# as they cover motor cortex
C_sensors = ['FC5', 'FC1', 'FC2', 'FC6', 'C3', 'C4', 'CP5',
'CP1', 'CP2', 'CP6', 'FC3', 'FCz', 'FC4', 'C5', 'C1', 'C2',
'C6',
'CP3', 'CPz', 'CP4', 'FFC5h', 'FFC3h', 'FFC4h', 'FFC6h',
'FCC5h',
'FCC3h', 'FCC4h', 'FCC6h', 'CCP5h', 'CCP3h', 'CCP4h', 'CCP6h',
'CPP5h',
'CPP3h', 'CPP4h', 'CPP6h', 'FFC1h', 'FFC2h', 'FCC1h', 'FCC2h',
'CCP1h',
'CCP2h', 'CPP1h', 'CPP2h']
cnt = cnt.pick_channels(C_sensors)
# Further preprocessings
log.info("Resampling...")
cnt = resample_cnt(cnt, 250.0)
print("REREFERENCING")
log.info("Highpassing...")
cnt = mne_apply(lambda a: highpass_cnt(a, low_cut_hz, cnt.info['sfreq'], filt_order=3, axis=1),cnt)
log.info("Standardizing...")
cnt = mne_apply(lambda a: exponential_running_standardize(a.T, factor_new=1e-3,init_block_size=1000,eps=1e-4).T,cnt)
# Trial interval, start at -500 already, since improved decoding for networks
ival = [-500, 4000]
dataset = create_signal_target_from_raw_mne(cnt, marker_def, ival)
dataset.X = dataset.X[clean_trial_mask]
dataset.y = dataset.y[clean_trial_mask]
return dataset.X, dataset.y
def load_bbci_data(filename, low_cut_hz, debug=False):
load_sensor_names = None
if debug:
load_sensor_names = ['C3', 'C4', 'C2']
loader = BBCIDataset(filename, load_sensor_names=load_sensor_names)
log.info("Loading data...")
cnt = loader.load()
log.info("Cutting trials...")
marker_def = OrderedDict([('Right Hand', [1]), (
'Left Hand',
[2],
), ('Rest', [3]), ('Feet', [4])])
clean_ival = [0, 4000]
set_for_cleaning = create_signal_target_from_raw_mne(
cnt, marker_def, clean_ival)
clean_trial_mask = np.max(np.abs(set_for_cleaning.X), axis=(1, 2)) < 800
log.info("Clean trials: {:3d} of {:3d} ({:5.1f}%)".format(
np.sum(clean_trial_mask), len(set_for_cleaning.X),
np.mean(clean_trial_mask) * 100))
# lets convert to millivolt for numerical stability of next operations
C_sensors = [
'FC5', 'FC1', 'FC2', 'FC6', 'C3', 'C4', 'CP5', 'CP1', 'CP2', 'CP6',
'FC3', 'FCz', 'FC4', 'C5', 'C1', 'C2', 'C6', 'CP3', 'CPz', 'CP4',
'FFC5h', 'FFC3h', 'FFC4h', 'FFC6h', 'FCC5h', 'FCC3h', 'FCC4h', 'FCC6h',
'CCP5h', 'CCP3h', 'CCP4h', 'CCP6h', 'CPP5h', 'CPP3h', 'CPP4h', 'CPP6h',
'FFC1h', 'FFC2h', 'FCC1h', 'FCC2h', 'CCP1h', 'CCP2h', 'CPP1h', 'CPP2h'
]
if debug:
C_sensors = load_sensor_names
cnt = cnt.pick_channels(C_sensors)
cnt = mne_apply(lambda a: a * 1e6, cnt)
log.info("Resampling...")
cnt = resample_cnt(cnt, 250.0)
log.info("Highpassing...")
cnt = mne_apply(
lambda a: highpass_cnt(
a, low_cut_hz, cnt.info['sfreq'], filt_order=3, axis=1), cnt)
log.info("Standardizing...")
cnt = mne_apply(
lambda a: exponential_running_standardize(
a.T, factor_new=1e-3, init_block_size=1000, eps=1e-4).T, cnt)
ival = [-500, 4000]
dataset = create_signal_target_from_raw_mne(cnt, marker_def, ival)
return dataset
y_labels = raw_labels['y_label']
del raw_data, raw_labels
###########################################################################
### (3) Preprocessing #####################################################
###########################################################################
from braindecode.datautil.signalproc import lowpass_cnt, highpass_cnt, exponential_running_standardize
for ii in range(
0, 60): # change according to the number of trials (default = 60)
# 1. Data reconstruction
temp_data = FeatVect[ii, :, :]
temp_data = temp_data.transpose()
# 2. Lowpass filtering
lowpassed_data = lowpass_cnt(temp_data, 13, 200, filt_order=3)
# 3. Highpass filtering
bandpassed_data = highpass_cnt(lowpassed_data, 8, 200, filt_order=3)
# 4. Exponential running standardization
ExpRunStand_data = exponential_running_standardize(
bandpassed_data,
factor_new=0.001,
init_block_size=None,
eps=0.0001)
# 5. Renewal preprocessed data
ExpRunStand_data = ExpRunStand_data.transpose()
FeatVect[ii, :, :] = ExpRunStand_data
del temp_data, lowpassed_data, bandpassed_data, ExpRunStand_data
###########################################################################
### (3) Convert data to braindecode format ################################
###########################################################################
# pytorch expects float 32 for input and int64 for labels.
