def test_Cospcovariances(): """Test fit CospCovariances""" x = np.random.randn(2, 3, 1000) cov = CospCovariances() cov.fit(x) cov.fit_transform(x) assert_equal(cov.get_params(), dict(window=128, overlap=0.75, fmin=None, fmax=None, fs=None))
# Read epochs (train will be done only between 1 and 2s) # Testing will be done with a running classifier epochs = Epochs(raw, events, event_id, tmin, tmax, proj=True, picks=picks, baseline=None, preload=True, add_eeg_ref=False, verbose=False) labels = epochs.events[:, -1] - 2 # get epochs epochs_data = epochs.get_data() # compute cospectral covariance matrices fmin = 2.0 fmax = 40.0 cosp = CospCovariances(window=128, overlap=0.98, fmin=fmin, fmax=fmax, fs=160.0) covmats = cosp.fit_transform(epochs_data[:, ::4, :]) fr = np.fft.fftfreq(128)[0:64]*160 fr = fr[(fr >= fmin) & (fr <= fmax)] pv = [] Fv = [] # For each frequency bin, estimate the stats for i in range(covmats.shape[3]): p_test = PermutationTest(5000) p, F = p_test.test(covmats[:, :, :, i], labels) print(p_test.summary()) pv.append(p) Fv.append(F[0]) plot(fr, Fv, lw=2)
def test_Cospcovariances(): """Test fit CospCovariances""" x = np.random.randn(2, 3, 1000) cov = CospCovariances() cov.fit(x) cov.fit_transform(x)
def test_Cospcovariances(): """Test fit CospCovariances""" x = np.random.randn(2,3,1000) cov = CospCovariances() cov.fit(x) cov.fit_transform(x)