def preprocess_cnt(cnt, final_hz, half_before): log.info("Resampling...") cnt = resample_cnt(cnt, 250.0) 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, ) if half_before: cnt = resample_cnt(cnt, final_hz / 2.0) cnt = resample_cnt(cnt, final_hz) return cnt
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_data(filenames, sensor_names, name_to_start_codes, name_to_stop_codes, trial_ival, break_ival, min_break_length_ms, max_break_length_ms, input_time_length, filename_to_extra_args): all_sets = [] original_args = locals() for filename in filenames: kwargs = deepcopy(original_args) if filename in filename_to_extra_args: kwargs.update(filename_to_extra_args[filename]) log.info("Loading {:s}...".format(filename)) cnt = BBCIDataset(filename, load_sensor_names=sensor_names).load() cnt = cnt.drop_channels(['STI 014']) log.info("Resampling...") cnt = resample_cnt(cnt, 100) log.info("Standardizing...") cnt = mne_apply( lambda a: exponential_running_standardize( a.T, init_block_size=50).T, cnt) log.info("Transform to set...") full_set = (create_signal_target_with_breaks_from_mne( cnt, kwargs['name_to_start_codes'], kwargs['trial_ival'], kwargs['name_to_stop_codes'], kwargs['min_break_length_ms'], kwargs['max_break_length_ms'], kwargs['break_ival'], prepad_trials_to_n_samples=kwargs['input_time_length'], )) all_sets.append(full_set) return all_sets
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(self): cnt = self.set_loaders[0].load() for loader in self.set_loaders[1:]: next_cnt = loader.load() # always sample down to lowest common denominator if next_cnt.fs > cnt.fs: log.warning("Next set has larger sampling rate ({:d}) " "than before ({:d}), resampling next set".format( next_cnt.fs, cnt.fs)) next_cnt = resample_cnt(next_cnt, cnt.fs) if next_cnt.fs < cnt.fs: log.warning("Next set has smaller sampling rate ({:d}) " "than before ({:d}), resampling set so far".format( next_cnt.fs, cnt.fs)) cnt = resample_cnt(cnt, next_cnt.fs) cnt = concatenate_raws_with_events(cnt, next_cnt) return cnt
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
def import_EEGData_test(start=0, end=9, dir='../data_HGD/test/'): X, y = [], [] for i in range(start, end): dataFile = str(dir + str(i + 1) + '.mat') print("File:", dataFile, " loading...") cnt = BBCIDataset(filename=dataFile, load_sensor_names=None).load() 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 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) cnt = resample_cnt(cnt, 250.0) 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) dataset.X = dataset.X[clean_trial_mask] dataset.X = dataset.X[:, :, np.newaxis, :] dataset.y = dataset.y[clean_trial_mask] dataset.y = dataset.y[:, np.newaxis] X.extend(dataset.X) y.extend(dataset.y) X = data_in_one(np.array(X)) y = np.array(y) print("X:", X.shape) print("y:", y.shape) dataset = EEGDataset(X, y) return dataset
def resample(cnt, fs): return resample_cnt(cnt, fs)