# Only keep the channel we're interested in
ch_data = np.array(eeg_data)[:, index_channel]
# Update EEG buffer
eeg_buffer, filter_state = BCIw.update_buffer(
eeg_buffer, ch_data, notch=True,
filter_state=filter_state)
""" 3.2 COMPUTE FEATURES AND CLASSIFY """
# Get newest samples from the buffer
data_epoch = BCIw.get_last_data(eeg_buffer,
epoch_length * fs)
# Compute features
feat_vector = BCIw.compute_feature_vector(data_epoch, fs)
y_hat = BCIw.test_classifier(classifier,
feat_vector.reshape(1, -1), mu_ft,
std_ft)
print(y_hat)
decision_buffer, _ = BCIw.update_buffer(decision_buffer,
np.reshape(y_hat, (-1, 1)))
""" 3.3 VISUALIZE THE DECISIONS """
plotter_decision.update_plot(decision_buffer)
plt.pause(0.00001)
except KeyboardInterrupt:
print('Closed!')
eeg_data, timestamp = inlet.pull_chunk(timeout=1,
max_samples=int(
shift_length * fs))
# Only keep the channel we're interested in
ch_data = np.array(eeg_data)[:, index_channel]
# Update EEG buffer
eeg_buffer, filter_state = BCIw.update_buffer(
eeg_buffer, ch_data, notch=True, filter_state=filter_state)
""" 3.2 COMPUTE FEATURES AND CLASSIFY """
# Get newest samples from the buffer
data_epoch = BCIw.get_last_data(eeg_buffer, epoch_length * fs)
# Compute features
feat_vector = BCIw.compute_feature_vector(data_epoch, fs)
y_hat = BCIw.test_classifier(classifier,
feat_vector.reshape(1, -1), mu_ft,
std_ft)
print(y_hat)
decision_buffer, _ = BCIw.update_buffer(decision_buffer,
np.reshape(y_hat, (-1, 1)))
""" 3.3 VISUALIZE THE DECISIONS """
plotter_decision.update_plot(decision_buffer)
plt.pause(0.00001)
except KeyboardInterrupt:
print('Closed!')