def feature_extraction(data, sfreq): #What features do I want??? #Kurtosis #line-length #entropy #Perform feature extraction for 2 second segments kurt = np.zeros((data.shape[0],data.shape[1]//2//sfreq)) s = np.zeros((data.shape[0],data.shape[1]//2//sfreq)) H = np.zeros((data.shape[0],data.shape[1]//2//sfreq)) segments = np.zeros((data.shape[0],data.shape[1]//2//sfreq,sfreq*2)) for n in range(0,data.shape[1]//2//sfreq): cur_data = data[:,n*sfreq*2:(n+1)*sfreq*2] segments[:,n,:] = cur_data kurt[:,n] = univariate.compute_kurtosis(cur_data) s[:,n] = univariate.compute_line_length(cur_data) H[:,n] = univariate.compute_spect_entropy(sfreq, cur_data, psd_method='welch') #Average across channels kurt_avg = np.mean(kurt,axis=0) s_avg = np.mean(s,axis=0)*np.power(10,6) H_avg = np.mean(H,axis=0) return kurt_avg, s_avg, H_avg
def test_kurtosis():
expected = np.array([1141. / 361, 197. / 121])
assert_almost_equal(compute_kurtosis(data1), expected)