def get_bci_iii_3a(path, subject=1, session=1, task=1): if task > 1: print "This dataset has just one task!" return None if session > 1: print "This dataset has only one session per subject!" return None description = 'BCI III Competition - Dataset IIIa' ch_names = [] ch_names = ch_names + ['ch' + str(i + 1) for i in range(60)] ch_names = ch_names + ['stim'] ch_types = [] ch_types = ch_types + ['eeg'] * 60 ch_types = ch_types + ['stim'] event_id = dict(left=1, right=2, feet=3, tongue=4) filename = 'subject' + str(subject) + '.mat' filepath = path + filename mat = loadmat(filepath) fs = mat['fs'] signal = mat['signal'].T.astype('float') # nchannels, nsamples raw = conversion.create_mne_raw(signal, fs, ch_names, ch_types, description) return raw, event_id
def get_brain_invaders_p300(path, subject=1, session=1, task=1): if task > 1: print "This dataset has just one task!" return None if session > 1: print "This dataset has just one session per subject!" return None description = 'P300 from Brain Invaders' ch_names = [] ch_names = ch_names + ['ch' + str(i + 1) for i in range(32)] ch_names = ch_names + ['stim'] event_id = dict(target=2, nontarget=1) ch_types = [] ch_types = ch_types + ['eeg'] * 32 ch_types = ch_types + ['stim'] filename = 'subject' + "{0:0>2}".format(subject) + '.mat' filepath = path + filename mat = loadmat(filepath) fs = mat['fs'] signal = mat['signal'].T.astype('float') # nchannels, nsamples raw = conversion.create_mne_raw(signal, fs, ch_names, ch_types, description) return raw, event_id
def get_bci_iii_p300(path, subject=1, session=1, task=1): if task > 1: print "This dataset has just one task!" return None if session > 2: print "This dataset has just two sessions per subject!" return None description = 'BCI III Competition - Dataset II (P300)' ch_names = [] ch_names = ch_names + ['ch' + str(i + 1) for i in range(64)] ch_names = ch_names + ['stim'] event_id = dict(target=2, nontarget=1) ch_types = [] ch_types = ch_types + ['eeg'] * 64 ch_types = ch_types + ['stim'] filename = 'Subject' + str(subject) + '_part' + str(session) + '.mat' filepath = path + filename mat = loadmat(filepath) fs = mat['fs'] signal = mat['signal'].T.astype('float') # nchannels, nsamples raw = conversion.create_mne_raw(signal, fs, ch_names, ch_types, description) return raw, event_id
def get_bnci_erp_als(path, subject=1, session=1, task=1): if task > 1: print "This dataset has just one task!" return None if session > 1: print "This dataset has just one session per subject!" return None description = 'P300 speller with ALS patients (from BNCI)' ch_names = [] mat = loadmat(path + 'chnames.mat') nchannels = mat['chnames'].shape[1] ch_names = [] for n in range(nchannels): ch_names.append(mat['chnames'][0][n][0]) ch_names += ['stim'] ch_types = [] ch_types = ch_types + ['eeg'] * nchannels ch_types = ch_types + ['stim'] event_id = dict(target=2, nontarget=1) filename = 'A0' + str(subject) + '.mat' filepath = path + filename mat = loadmat(filepath) fs = mat['fs'] signal = mat['signal'].T.astype('float') # nchannels, nsamples raw = conversion.create_mne_raw(signal, fs, ch_names, ch_types, description) return raw, event_id
def get_bnci_individual_imagery(path, subject=1, session=1, task=1): if task > 1: print "This dataset has only one task!" return None if session > 1: print "This dataset has just two sessions per subject!" return None description = 'Individual motor imagery database (from BNCI)' ch_names = [] mat = loadmat(path + 'chnames.mat') nchannels = mat['chnames'].shape[1] ch_names = [] for n in range(nchannels): ch_names.append(mat['chnames'][0][n][0]) ch_names += ['stim'] ch_types = [] ch_types = ch_types + ['eeg'] * nchannels ch_types = ch_types + ['stim'] event_id = dict(word=1, sub=2, nav=3, hand=4, feet=5) subjects = ['A', 'C', 'D', 'E', 'F', 'G', 'H', 'J', 'L'] filename = str(subjects[subject]) + str(session) + '.mat' filepath = path + filename mat = loadmat(filepath) fs = mat['fs'] signal = mat['signal'].T.astype('float') # nchannels, nsamples raw = conversion.create_mne_raw(signal, fs, ch_names, ch_types, description) return raw, event_id
def get_bnci_two_class_motor_imagery(path, subject=1, session=1, task=1): if task > 1: print "This dataset has only one task!" return None if session > 1: print "This dataset has only one session per subject!" return None description = 'Two class motor imagery database (from BNCI)' ch_names = [] ch_names = ch_names + ['ch' + str(i + 1) for i in range(15)] ch_names = ch_names + ['stim'] ch_types = [] ch_types = ch_types + ['eeg'] * 15 ch_types = ch_types + ['stim'] event_id = dict(right=1, feet=2) # getting the training data for this subject if subject < 10: filename = 'S0' + str(subject) + 'T.mat' else: filename = 'S' + str(subject) + 'T.mat' filepath = path + filename mat = loadmat(filepath) fs = mat['fs'] signal = mat['signal'].T.astype('float') # nchannels, nsamples raw = conversion.create_mne_raw(signal, fs, ch_names, ch_types, description) return raw, event_id
def get_bci_iii_4a(path, subject=1, session=1, task=1): if task > 1: print "This dataset has just one task!" return None if session > 1: print "This dataset has only one session per subject!" return None description = 'BCI III Competition - Dataset IVa' ch_names = [] mat = loadmat(path + 'chnames.mat') nchannels = mat['chnames'].shape[1] ch_names = [] for n in range(nchannels): ch_names.append(mat['chnames'][0][n][0]) ch_names += ['stim'] ch_types = [] ch_types = ch_types + ['eeg'] * nchannels ch_types = ch_types + ['stim'] event_id = dict(hand=1, feet=2) filename = 'subject' + str(subject) + '.mat' filepath = path + filename mat = loadmat(filepath) fs = mat['fs'] signal = mat['signal'].T.astype('float') # nchannels, nsamples raw = conversion.create_mne_raw(signal, fs, ch_names, ch_types, description) return raw, event_id
def get_bci_iv_2a(path, subject=1, session=1, task=1): if task > 1: print "This dataset has just one task!" return None if session > 2: print "This dataset has just two sessions per subject!" return None description = 'BCI IV Competition - Dataset 2a' ch_names = [] mat = loadmat(path + 'chnames.mat') nchannels = mat['chnames'].shape[1] ch_names = [] for n in range(nchannels): ch_names.append(mat['chnames'][0][n][0]) ch_names = ch_names + ['eog' + str(i + 1) for i in range(3)] ch_names += ['stim'] ch_types = [] ch_types = ch_types + ['eeg'] * 22 ch_types = ch_types + ['eog'] * 3 ch_types = ch_types + ['stim'] event_id = dict(left=1, right=2, feet=3, tongue=4) suffix = ['T', 'E'][session - 1] filename = 'A0' + str(subject) + suffix + '.mat' filepath = path + filename mat = loadmat(filepath) fs = mat['fs'] signal = mat['signal'].T.astype('float') # nchannels, nsamples raw = conversion.create_mne_raw(signal, fs, ch_names, ch_types, description) return raw, event_id