Python Approach.define_bad_epochs Exemples

Exemple #1

# 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])

Exemple #2

    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)

Exemple #3

Fichier : applyCSPLDAapproach_crossvalidation.py Projet : rafaelmendes/bci_ml

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

Exemple #4

Fichier : plot_fft_amp_along_epoch.py Projet : rafaelmendes/bci_ml

	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)