def test_SSVEP_paradigm(self):
paradigm = SSVEP(n_classes=None)
dataset = FakeDataset(event_list=["13", "15", "17", "19"],
paradigm="ssvep")
X, labels, metadata = paradigm.get_data(dataset, subjects=[1])
# Verify that they have the same length
self.assertEqual(len(X), len(labels), len(metadata))
# X must be a 3D array
self.assertEqual(len(X.shape), 3)
# labels must contain 4 values, defined in the dataset
self.assertEqual(len(np.unique(labels)), 4)
# metadata must have subjets, sessions, runs
self.assertTrue("subject" in metadata.columns)
self.assertTrue("session" in metadata.columns)
self.assertTrue("run" in metadata.columns)
# Only one subject in the metadata
self.assertEqual(np.unique(metadata.subject), 1)
# We should have two sessions in the metadata
self.assertEqual(len(np.unique(metadata.session)), 2)
# should return epochs
epochs, _, _ = paradigm.get_data(dataset,
subjects=[1],
return_epochs=True)
self.assertIsInstance(epochs, BaseEpochs)
def test_SSVEP_singlepass(self):
# Accept only single pass filter
paradigm = SSVEP(fmin=2, fmax=25)
dataset = FakeDataset(paradigm="ssvep")
X, labels, metadata = paradigm.get_data(dataset, subjects=[1])
# Verify that they have the same length
self.assertEqual(len(X), len(labels), len(metadata))
# X must be a 3D array
self.assertEqual(len(X.shape), 3)
# labels must contain all 3 classes of dataset,
# as n_classes is "None" by default (taking all classes)
self.assertEqual(len(np.unique(labels)), 3)
# should return epochs
epochs, _, _ = paradigm.get_data(dataset, subjects=[1], return_epochs=True)
self.assertIsInstance(epochs, BaseEpochs)
def test_SSVEP_singlepass(self):
# Accept only single pass filter
paradigm = SSVEP(fmin=2, fmax=25)
dataset = FakeDataset(paradigm='ssvep')
X, labels, metadata = paradigm.get_data(dataset, subjects=[1])
# Verify that they have the same length
self.assertEqual(len(X), len(labels), len(metadata))
# X must be a 3D array
self.assertEqual(len(X.shape), 3)
# labels must contain 2 values, as n_classes = 2 by default
self.assertEqual(len(np.unique(labels)), 2)
def test_SSVEP_paradigm(self):
paradigm = SSVEP(n_classes=None)
dataset = FakeDataset(event_list=['13', '15', '17', '19'],
paradigm='ssvep')
X, labels, metadata = paradigm.get_data(dataset, subjects=[1])
# Verify that they have the same length
self.assertEqual(len(X), len(labels), len(metadata))
# X must be a 3D array
self.assertEqual(len(X.shape), 3)
# labels must contain 4 values, defined in the dataset
self.assertEqual(len(np.unique(labels)), 4)
# metadata must have subjets, sessions, runs
self.assertTrue('subject' in metadata.columns)
self.assertTrue('session' in metadata.columns)
self.assertTrue('run' in metadata.columns)
# Only one subject in the metadata
self.assertEqual(np.unique(metadata.subject), 1)
# We should have two sessions in the metadata
self.assertEqual(len(np.unique(metadata.session)), 2)
##############################################################################
# Get data (optional)
# -------------------
#
# To get access to the EEG signals downloaded from the dataset, you could
# use `dataset._get_single_subject_data(subject_id) to obtain the EEG under
# an MNE format, stored in a dictionary of sessions and runs.
# Otherwise, `paradigm.get_data(dataset=dataset, subjects=[subject_id])`
# allows to obtain the EEG data in scikit format, the labels and the meta
# information.
X_all, labels_all, meta_all = [], [], []
for d in datasets:
# sessions = d._get_single_subject_data(2)
X, labels, meta = paradigm.get_data(dataset=d, subjects=[2])
X_all.append(X)
labels_all.append(labels)
meta_all.append(meta)
##############################################################################
# Evaluation
# ----------
#
# The evaluation will return a dataframe containing a single AUC score for
# each subject / session of the dataset, and for each pipeline.
overwrite = True # set to True if we want to overwrite cached results
evaluation = CrossSessionEvaluation(paradigm=paradigm,
datasets=datasets,
