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 = 2.5, 4.5 # 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.setPathToVal(DATA_VAL_PATH, VAL_EVENTS_PATH)
ap.setValidChannels([-1])
ap.set_balance_epochs(False)
autoscore = ap.trainModel()
valscore = ap.validateModel()
print autoscore
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 = 2.5,4.5 # 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.setPathToVal(DATA_VAL_PATH, VAL_EVENTS_PATH) ap.setValidChannels([-1]) ap.set_balance_epochs(False) autoscore = ap.trainModel() valscore = ap.validateModel() print autoscore print valscore
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, 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
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
SAMPLING_FREQ = 250.0
# FILTER SPEC
LOWER_CUTOFF = 8.
UPPER_CUTOFF = 30.
FILT_ORDER = 7
# EPOCH EXTRACTION CONFIG:
EVENT_IDS = [769, 770, 771, 772]
T_MIN, T_MAX = 0.5, 2.5 # 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.setValidChannels(range(22))
data, ev = ap.loadData(DATA_PATH, EVENTS_PATH)
epochs, labels = ap.loadEpochs(data, ev)
epochs = ap.preProcess(epochs)
idx_left = np.where(labels == EVENT_IDS[0])[0]
idx_right = np.where(labels == EVENT_IDS[1])[0]
idx_foot = np.where(labels == EVENT_IDS[2])[0]
idx_tongue = np.where(labels == EVENT_IDS[3])[0]
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
SAMPLING_FREQ = 125.0
# FILTER SPEC
LOWER_CUTOFF = 8.
UPPER_CUTOFF = 15.
FILT_ORDER = 5
# EPOCH EXTRACTION CONFIG:
EVENT_IDS = [1, 2]
T_MIN, T_MAX = -4, 4 # 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.setPathToVal(DATA_VAL_PATH, VAL_EVENTS_PATH)
ap.setValidChannels([-1])
data, events = ap.loadData(DATA_CAL_PATH, CAL_EVENTS_PATH)
data = ap.preProcess(data)
epochs, labels = ap.loadEpochs(data, events)
SAMPLING_FREQ = 250.0
# FILTER SPEC
LOWER_CUTOFF = 8.
UPPER_CUTOFF = 30.
FILT_ORDER = 7
# EPOCH EXTRACTION CONFIG:
EVENT_IDS = [769, 770, 771, 772]
T_MIN, T_MAX = 0.5, 2.5 # 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.setValidChannels(range(22))
data, ev = ap.loadData(DATA_PATH, EVENTS_PATH)
epochs, labels = ap.loadEpochs(data, ev)
epochs = ap.preProcess(epochs)
idx_left = np.where(labels == EVENT_IDS[0])[0]
idx_right = np.where(labels == EVENT_IDS[1])[0]
idx_foot = np.where(labels == EVENT_IDS[2])[0]
SAMPLING_FREQ = 250.0 # FILTER SPEC LOWER_CUTOFF = 8. UPPER_CUTOFF = 30. FILT_ORDER = 5 # EPOCH EXTRACTION CONFIG: EVENT_IDS = [1,2] T_MIN, T_MAX = 2.5,4.5 # 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(range(23)) data, events = ap.loadData(DATA_CAL_PATH, CAL_EVENTS_PATH) data = ap.preProcess(data) c3_idx = 7 front=1 left = 6 right = 8
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 = 3,5 # 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