# print(heatmap) # np.savetxt("timepoints_sub5_18reps.csv", heatmap, delimiter=',') n_comp = 4 csp = CSP(n_components=n_comp, reg=None, log=True, norm_trace=False) #x, y = data.load_all(cut=[start, end]) #y = np.where(y==1)[1] #print(x.shape) print(timepoints) x, y = data.load_single([5], cut=False) x = x[0] y = np.where(y[0] == 1)[1] for i in range(steps): csp.fit_transform(x[:, :, timepoints[i]:timepoints[i] + p_size], y) for j in range(n_comp): layout = read_layout('EEG1005') csp.plot_patterns(info, colorbar=False, layout=layout, ch_type='eeg', size=1.5, show=False, show_names=False, components=j, title="{}".format(int( (timepoints[i] - 768) / 0.512))) #plt.savefig('fig/sub9_time_{}_comp_{}.png'.format(i, j))
clf = Pipeline([('CSP', csp), ('MLP', mlp)]) #clf.set_params(CSP__reg=0.5) for i in range(100): shuffle(labels) scores = cross_val_score(clf, epochs_data_train, labels, cv=10, n_jobs=1) accs[sub - 1 if sub < 10 else sub - 2, i] = np.mean(scores) # Printing the results # print(labels) # plot CSP patterns estimated on full data for visualization csp.fit_transform(epochs_data, labels) layout = read_layout('EEG1005') csp.plot_patterns(info, colorbar=True, layout=layout, ch_type='eeg', size=1.5, show=False, show_names=False, scalings=1, title="4 first CSP components") plt.savefig('fig/csp_4comp_sub5_cbar.png') np.savetxt("csp_mlp_scores.csv", accs, delimiter=',')