def test_FilterBankSSVEP_filters(self):
# can work with filter bank
paradigm = FilterBankSSVEP(filters=[(10.5, 11.5), (12.5, 13.5)])
dataset = FakeDataset(event_list=["13", "15", "17"], paradigm="ssvep")
X, labels, metadata = paradigm.get_data(dataset, subjects=[1])
# X must be a 4D array with d=2 as last dimension for the 2 filters
self.assertEqual(len(X.shape), 4)
self.assertEqual(X.shape[-1], 2)
# should return epochs
epochs, _, _ = paradigm.get_data(dataset, subjects=[1], return_epochs=True)
self.assertIsInstance(epochs, BaseEpochs)
def test_FilterBankSSVEP_paradigm(self):
# FilterBankSSVEP with all events
paradigm = FilterBankSSVEP(n_classes=None)
dataset = FakeDataset(event_list=["13", "15", "17", "19"], paradigm="ssvep")
X, labels, metadata = paradigm.get_data(dataset, subjects=[1])
# X must be a 4D array
self.assertEqual(len(X.shape), 4)
# X must be a 4D array with d=4 as last dimension for the 4 events
self.assertEqual(X.shape[-1], 4)
# should return epochs
epochs, _, _ = paradigm.get_data(dataset, subjects=[1], return_epochs=True)
self.assertIsInstance(epochs, BaseEpochs)
def test_FilterBankSSVEP_filters(self):
# can work with filter bank
paradigm = FilterBankSSVEP(filters=[(10.5, 11.5), (12.5, 13.5)])
dataset = FakeDataset(event_list=['13', '15', '17'], paradigm='ssvep')
X, labels, metadata = paradigm.get_data(dataset, subjects=[1])
# X must be a 4D array with d=2 as last dimension for the 2 filters
self.assertEqual(len(X.shape), 4)
self.assertEqual(X.shape[-1], 2)
def test_FilterBankSSVEP_paradigm(self):
# FilterBankSSVEP with all events
paradigm = FilterBankSSVEP(n_classes=None)
dataset = FakeDataset(event_list=['13', '15', '17', '19'],
paradigm='ssvep')
X, labels, metadata = paradigm.get_data(dataset, subjects=[1])
# X must be a 4D array
self.assertEqual(len(X.shape), 4)
# X must be a 4D array with d=4 as last dimension for the 4 events
self.assertEqual(X.shape[-1], 4)
sample, timestamp = inlet.pull_sample()
sample = np.array(sample)
return np.column_stack((sample, time_window))
###############################################################################
## Option to online retraining
retrain = True
## FIles to offilne base
dataset = SSVEPExo(subjects=11)
interval = dataset.interval
paradigm_fb = FilterBankSSVEP(filters=None, n_classes=4)
filtered_db = paradigm_fb.get_data(dataset, return_epochs=False)
pipeline = make_pipeline(
ExtendedSSVEPSignal(),
Covariances(estimator="lwf"),
TangentSpace(),
LogisticRegression(solver="lbfgs", multi_class="auto"),
)
model = pipeline.fit()
# End of offline training
# EEG stream on the lab network
print("looking for an EEG stream...")
streams = resolve_stream('name', 'openvibeSignal')
