def test_FilterBankLeftRightImagery_paradigm(self):
# can work with filter bank
paradigm = FilterBankLeftRightImagery()
dataset = FakeDataset(event_list=['left_hand', 'right_hand'])
X, labels, metadata = paradigm.get_data(dataset, subjects=[1])
# X must be a 4D Array
self.assertEqual(len(X.shape), 4)
self.assertEqual(X.shape[-1], 6)
def test_FilterBankLeftRightImagery_paradigm(self):
# can work with filter bank
paradigm = FilterBankLeftRightImagery()
dataset = FakeDataset(event_list=["left_hand", "right_hand"], paradigm="imagery")
X, labels, metadata = paradigm.get_data(dataset, subjects=[1])
# X must be a 4D Array
self.assertEqual(len(X.shape), 4)
self.assertEqual(X.shape[-1], 6)
# should return epochs
epochs, _, _ = paradigm.get_data(dataset, subjects=[1], return_epochs=True)
self.assertIsInstance(epochs, BaseEpochs)
# broadband filters
filters = [[8, 35]]
paradigm = LeftRightImagery(filters=filters)
evaluation = CrossSessionEvaluation(paradigm=paradigm,
datasets=datasets,
suffix='examples',
overwrite=overwrite)
results = evaluation.process(pipelines)
# cashed results might return other pipelines
results = results[results.pipeline == 'CSP + LDA']
# bank of 6 filter, by 4 Hz increment
filters = [[8, 12], [12, 16], [16, 20], [20, 24], [24, 28], [28, 35]]
paradigm = FilterBankLeftRightImagery()
evaluation = CrossSessionEvaluation(paradigm=paradigm,
datasets=datasets,
suffix='examples',
overwrite=overwrite)
results_fb = evaluation.process(pipelines_fb)
###############################################################################
# After processing the two, we simply concatenate the results.
results = pd.concat([results, results_fb])
##############################################################################
# Plot Results
# ----------------
#
datasets = [BNCI2014001()]
overwrite = False # set to True if we want to overwrite cached results
# broadband filters
fmin = 8
fmax = 35
paradigm = LeftRightImagery(fmin=fmin, fmax=fmax)
evaluation = CrossSessionEvaluation(paradigm=paradigm,
datasets=datasets,
suffix='examples',
overwrite=overwrite)
results = evaluation.process(pipelines)
# bank of 6 filter, by 4 Hz increment
filters = [[8, 12], [12, 16], [16, 20], [20, 24], [24, 28], [28, 35]]
paradigm = FilterBankLeftRightImagery(filters=filters)
evaluation = CrossSessionEvaluation(paradigm=paradigm,
datasets=datasets,
suffix='examples',
overwrite=overwrite)
results_fb = evaluation.process(pipelines_fb)
###############################################################################
# After processing the two, we simply concatenate the results.
results = pd.concat([results, results_fb])
##############################################################################
# Plot Results
# ----------------
#