def get_data(): import os os.sys.path.append('/home/schirrmr/braindecode/code/braindecode/') from braindecode.datautil.trial_segment import create_signal_target_from_raw_mne from braindecode.datasets.bbci import BBCIDataset from braindecode.mne_ext.signalproc import mne_apply, resample_cnt from braindecode.datautil.signalproc import exponential_running_standardize subject_id = 4 # 1-14 loader = BBCIDataset( '/data/schirrmr/schirrmr/HGD-public/reduced/train/{:d}.mat'.format( subject_id), load_sensor_names=['C3']) cnt = loader.load() cnt = cnt.drop_channels(['STI 014']) from collections import OrderedDict marker_def = OrderedDict([('Right Hand', [1]), ( 'Left Hand', [2], ), ('Rest', [3]), ('Feet', [4])]) # Here you can choose a larger sampling rate later # Right now chosen very small to allow fast initial experiments cnt = resample_cnt(cnt, new_fs=500) cnt = mne_apply( lambda a: exponential_running_standardize( a.T, factor_new=1e-3, init_block_size=1000, eps=1e-4).T, cnt) ival = [0, 2000] # ms to cut trial dataset = create_signal_target_from_raw_mne(cnt, marker_def, ival) return dataset.X, dataset.y
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_cnt(file_path, channel_names, clean_on_all_channels=True): # if we have to run the cleaning procedure on all channels, putting # load_sensor_names to None will assure us the BBCIDataset class will # load all possible sensors if clean_on_all_channels is True: channel_names = None # create the loader object for BBCI standard loader = BBCIDataset(file_path, load_sensor_names=channel_names) # load data return loader.load()
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