# EPOCH EXTRACTION CONFIG: EVENT_IDS = [1, 2] T_MIN, T_MAX = 3, 5 # time before event, time after event CSP_N = 12 ap = Approach() ap.defineApproach(SAMPLING_FREQ, LOWER_CUTOFF, UPPER_CUTOFF, FILT_ORDER, CSP_N, EVENT_IDS, T_MIN, T_MAX) ap.setPathToCal(DATA_CAL_PATH, CAL_EVENTS_PATH) ap.setValidChannels([-1]) ap.define_bad_epochs(100) autoscore = ap.trainModel() crossvalscore = ap.cross_validate_model(10, 0.2) print autoscore print crossvalscore ## test on single epoch import numpy as np data, events = ap.loadData(DATA_CAL_PATH, CAL_EVENTS_PATH) buf = np.array([data.shape[0], 250])
T_MIN += increment T_MAX += increment t.extend([T_MIN]) CSP_N = 12 ap = Approach() ap.defineApproach(SAMPLING_FREQ, LOWER_CUTOFF, UPPER_CUTOFF, FILT_ORDER, CSP_N, EVENT_IDS, T_MIN, T_MAX) ap.setPathToCal(DATA_CAL_PATH, CAL_EVENTS_PATH) ap.setValidChannels(range(16)) ap.define_bad_epochs(50, None) data, events = ap.loadData(DATA_CAL_PATH, CAL_EVENTS_PATH) ref_channel = 8 # fcz data = ap.preProcess(data) data = data[:, :] - data[ref_channel] # nch = data.shape[0] # Id = np.identity(nch) # W = Id - (1.0 / nch) * np.dot(Id, Id.T) # data = np.dot(W, data) epochs, labels = ap.loadEpochs(data, events)
SAMPLING_FREQ = 125.0 # FILTER SPEC LOWER_CUTOFF = 8. UPPER_CUTOFF = 30. FILT_ORDER = 5 # EPOCH EXTRACTION CONFIG: EVENT_IDS = [1,2] T_MIN, T_MAX = 3,8 # time before event, time after event CSP_N = 8 ap = Approach() ap.defineApproach(SAMPLING_FREQ, LOWER_CUTOFF, UPPER_CUTOFF, FILT_ORDER, CSP_N, EVENT_IDS, T_MIN, T_MAX) ap.setPathToCal(DATA_CAL_PATH, CAL_EVENTS_PATH) ap.setValidChannels([-1]) ap.define_bad_epochs(100) autoscore = ap.trainModel() crossvalscore = ap.cross_validate_model(10, 0.2) print 'SelfValidation result: ', autoscore print 'Cross Validation result: ', crossvalscore
T_MIN += increment T_MAX += increment t.extend([T_MIN]) CSP_N = 12 ap = Approach() ap.defineApproach(SAMPLING_FREQ, LOWER_CUTOFF, UPPER_CUTOFF, FILT_ORDER, CSP_N, EVENT_IDS, T_MIN, T_MAX) ap.setPathToCal(DATA_CAL_PATH, CAL_EVENTS_PATH) ap.setValidChannels(range(16)) ap.define_bad_epochs(50, None) data, events = ap.loadData(DATA_CAL_PATH, CAL_EVENTS_PATH) ref_channel = 8 # fcz data = ap.preProcess(data) data = data[:,:] - data[ref_channel] # nch = data.shape[0] # Id = np.identity(nch) # W = Id - (1.0 / nch) * np.dot(Id, Id.T) # data = np.dot(W, data)