# script with <Ctrl-C>
print(' Press Ctrl-C in the console to break the While Loop')
try:
while True:
""" 1- ACQUIRE DATA """
eeg_data = mules_client.getdata(
shift_secs, False) # Obtain EEG data from MuLES
eeg_buffer = BCIw.updatebuffer(eeg_buffer,
eeg_data) # Update EEG buffer
# Get newest "testing samples" from the buffer
test_data = BCIw.getlastdata(
eeg_buffer, win_test_secs * params['sampling frequency'])
""" 2- COMPUTE FEATURES and CLASSIFY"""
# Compute features on "test_data"
feat_vector = BCIw.compute_feature_vector(
test_data, params['sampling frequency'])
y_hat = BCIw.classifier_test(classifier,
feat_vector.reshape(1, -1), mu_ft,
std_ft)
decision_buffer = BCIw.updatebuffer(decision_buffer,
np.reshape(y_hat, (-1, 1)))
""" 3- VISUALIZE THE DECISIONS"""
print(str(y_hat))
plotter_decision.updatePlot(
decision_buffer) # Plot the decision buffer
plt.pause(0.001)
except KeyboardInterrupt:
# The try/except structure allows to quit the while loop by aborting the
# script with <Ctrl-C>
print(' Press Ctrl-C in the console to break the While Loop')
try:
# The following loop does what we see in the diagram of Exercise 1:
# acquire data, compute features, visualize the raw EEG and the features
while True:
""" 1- ACQUIRE DATA """
eeg_data = mules_client.getdata(shift_secs, False) # Obtain EEG data from MuLES
eeg_data = eeg_data[:,[index_channel ,-1]] # Keep only one electrode (and the STATUS channel) for further analysis
eeg_buffer = BCIw.updatebuffer(eeg_buffer, eeg_data) # Update EEG buffer
""" 2- COMPUTE FEATURES """
# Get newest samples from the buffer
data_window = BCIw.getlastdata(eeg_buffer, win_test_secs * params['sampling frequency'])
# Compute features on "data_window"
feat_vector = BCIw.compute_feature_vector(data_window, params['sampling frequency'])
feat_buffer = BCIw.updatebuffer(feat_buffer, np.asarray([feat_vector])) # Update the feature buffer
""" 3- VISUALIZE THE RAW EEG AND THE FEATURES """
plotter_eeg.updatePlot(eeg_buffer) # Plot EEG buffer
plotter_feat.updatePlot((feat_buffer)) # Plot the feature buffer
plt.pause(0.001)
except KeyboardInterrupt:
mules_client.disconnect() # Close connection
# The try/except structure allows to quit the while loop by aborting the
# script with <Ctrl-C>
print(' Press Ctrl-C in the console to break the While Loop')
try:
while True:
eeg_data = mules_client.getdata(
shift_secs, False) # Obtain EEG data from MuLES
# Compute features on "eeg_data"
if args.csp:
#feat_vector = CSP_test(eeg_data, W_csp)
pass
else:
feat_matrix0t_vector = BCIw.compute_feature_vector(
eeg_data, params['sampling frequency'])
if (args.entropy):
entropy = calc_entropy(eeg_data)
feat_vector = np.hstack([feat_vector, entropy])
y_hat = model.predict(normalize(feat_vector.reshape(1, -1)))
# DECISION!!!!!!!!!
#plt.pause(0.001)
except KeyboardInterrupt:
mules_client.disconnect() # Close connection
BCIw.beep() # Beep sound
# The try/except structure allows to quit the while loop by aborting the
# script with <Ctrl-C>
print(' Press Ctrl-C in the console to break the While Loop')
try:
while True:
""" 1- ACQUIRE DATA """
eeg_data = mules_client.getdata(shift_secs, False)
eeg_buffer = BCIw.updatebuffer(eeg_buffer, eeg_data)
# Get newest "testing samples" from the buffer
test_data = BCIw.getlastdata(eeg_buffer, win_test_secs * params['sampling frequency'])
""" 2- COMPUTE FEATURES and CLASSIFY"""
# Compute features on "test_data"
feat_vector = BCIw.compute_feature_vector(test_data, params['sampling frequency'])
y_hat = BCIw.classifier_test(classifier, feat_vector, mu_ft, std_ft)
decision_buffer = BCIw.updatebuffer(decision_buffer, np.reshape(y_hat,(-1,1)))
""" 3- VISUALIZE THE DECISIONS"""
print str(y_hat)
plotter_decision.updatePlot(decision_buffer) # Plot the decision buffer
plt.pause(0.001)
except KeyboardInterrupt:
mules_client.disconnect() # Close connection
# The following loop does what we see in the diagram of Exercise 1:
# acquire data, compute features, visualize raw EEG and the features
while True:
""" 3.1 ACQUIRE DATA """
# Obtain EEG data from the LSL stream
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 """
# 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)
feat_buffer, _ = BCIw.update_buffer(feat_buffer,
np.asarray([feat_vector]))
""" 3.3 VISUALIZE THE RAW EEG AND THE FEATURES """
plotter_eeg.update_plot(eeg_buffer)
plotter_feat.update_plot(feat_buffer)
plt.pause(0.00001)
except KeyboardInterrupt:
print('Closing!')
# 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!')
BCIw.beep() # Beep sound
# The try/except structure allows to quit the while loop by aborting the
# script with <Ctrl-C>
print(' Press Ctrl-C in the console to break the While Loop')
try:
# The following loop does what we see in the diagram of Exercise 1:
# acquire data, compute features, visualize the raw EEG and the features
while True:
""" 1- ACQUIRE DATA """
eeg_data = mules_client.getdata(shift_secs, False) # Obtain EEG data from MuLES
eeg_buffer = BCIw.updatebuffer(eeg_buffer, eeg_data) # Update EEG buffer
""" 2- COMPUTE FEATURES """
# Get newest samples from the buffer
data_window = BCIw.getlastdata(eeg_buffer, win_test_secs * params['sampling frequency'])
# Compute features on "data_window"
feat_vector = BCIw.compute_feature_vector(data_window, params['sampling frequency'])
feat_buffer = BCIw.updatebuffer(feat_buffer, np.asarray([feat_vector])) # Update the feature buffer
""" 3- VISUALIZE THE RAW EEG AND THE FEATURES """
plotter_eeg.updatePlot(eeg_buffer) # Plot EEG buffer
plotter_feat.updatePlot((feat_buffer)) # Plot the feature buffer
plt.pause(0.001)
except KeyboardInterrupt:
mules_client.disconnect() # Close connection
