def test_LeftRightImagery_paradigm(self):
# with a good dataset
paradigm = LeftRightImagery()
dataset = FakeDataset(event_list=["left_hand", "right_hand"], paradigm="imagery")
X, labels, metadata = paradigm.get_data(dataset, subjects=[1])
self.assertEqual(len(np.unique(labels)), 2)
self.assertEqual(list(np.unique(labels)), ["left_hand", "right_hand"])
# should return epochs
epochs, _, _ = paradigm.get_data(dataset, subjects=[1], return_epochs=True)
self.assertIsInstance(epochs, BaseEpochs)
def test_LeftRightImagery_paradigm(self):
# with a good dataset
paradigm = LeftRightImagery()
dataset = FakeDataset(event_list=['left_hand', 'right_hand'])
X, labels, metadata = paradigm.get_data(dataset, subjects=[1])
self.assertEqual(len(np.unique(labels)), 2)
self.assertEqual(list(np.unique(labels)), ['left_hand', 'right_hand'])
def test_leftright_paradigm(self):
# we cant pass event to this class
paradigm = LeftRightImagery()
self.assertRaises(ValueError, LeftRightImagery, events=['a'])
# does not accept dataset with bad event
dataset = FakeDataset()
self.assertRaises(AssertionError, paradigm.get_data, dataset)
# with a good dataset
dataset = FakeDataset(event_list=['left_hand', 'right_hand'])
X, labels, metadata = paradigm.get_data(dataset, subjects=[1])
self.assertEquals(len(np.unique(labels)), 2)
self.assertEquals(list(np.unique(labels)), ['left_hand', 'right_hand'])
def get_cho_train_val_test(subject_id):
paradigm = LeftRightImagery()
dataset = Cho2017()
subjects = [subject_id]
X, y, metadata = paradigm.get_data(dataset=dataset, subjects=subjects)
y = np.where(y == 'left_hand', 0, y)
y = np.where(y == 'right_hand', 1, y)
X_train_val, X_test, y_train_val, y_test = train_test_split(
X, y, test_size=0.3) # test = 30%, same as paper
X_train, X_val, y_train, y_val = train_test_split(
X_train_val,
y_train_val,
test_size=global_vars.get('valid_set_fraction'))
train_set, valid_set, test_set = makeDummySignalTargets(
X_train, y_train, X_val, y_val, X_test, y_test)
return train_set, valid_set, test_set
def get_bci_iv_2b_train_val_test(subject_id):
paradigm = LeftRightImagery()
dataset = BNCI2014004()
subjects = [subject_id]
X, y, metadata = paradigm.get_data(dataset=dataset, subjects=subjects)
y = np.where(y == 'left_hand', 0, y)
y = np.where(y == 'right_hand', 1, y)
train_indexes = np.logical_or(metadata.values[:, 1] == 'session_0',
metadata.values[:, 1] == 'session_1',
metadata.values[:, 1] == 'session_2')
test_indexes = np.logical_or(metadata.values[:, 1] == 'session_3',
metadata.values[:, 1] == 'session_4')
X_train_val = X[train_indexes]
y_train_val = y[train_indexes]
X_test = X[test_indexes]
y_test = y[test_indexes]
X_train, X_val, y_train, y_val = train_test_split(
X_train_val,
y_train_val,
test_size=global_vars.get('valid_set_fraction'))
train_set, valid_set, test_set = makeDummySignalTargets(
X_train, y_train, X_val, y_val, X_test, y_test)
return train_set, valid_set, test_set
from pyriemann.classification import MDM
from sklearn.externals import joblib
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import roc_auc_score
from pyriemann.estimation import Covariances
from riemann_lab import power_means
import moabb
from moabb.datasets import BNCI2014001
from moabb.paradigms import LeftRightImagery
# load the data
subject = 1
dataset = BNCI2014001()
paradigm = LeftRightImagery()
X, labels, meta = paradigm.get_data(dataset, subjects=[subject])
X = X[meta['session'] == 'session_E']
covs = Covariances(estimator='oas').fit_transform(X)
labs = labels[meta['session'] == 'session_E']
# define the pipelines for classification -- MDM and MeansField classifier
pipelines = {}
pipelines['MDM'] = MDM()
plist = [1.00, 0.75, 0.50, 0.25, 0.10, 0.01, -0.01, -0.10, -0.25, -0.50, -0.75, -1.00]
pipelines['MeansField'] = power_means.MeanFieldClassifier(plist=plist)
# perform the KFold cross-validation procedure with stratified segments
# (same proportion of labels form each class on every fold)
n_splits = 5
kf = StratifiedKFold(n_splits)
scores = {}