feat_matrix1 = BCIw.compute_feature_matrix(eeg_epochs1, fs)
[classifier, mu_ft, std_ft,
score] = BCIw.train_classifier(feat_matrix0, feat_matrix1, 'SVM')
print(str(score * 100) + '% correctly predicted')
BCIw.beep()
""" 5. USE THE CLASSIFIER IN REAL-TIME"""
# Initialize the buffers for storing raw EEG and decisions
eeg_buffer = np.zeros((int(fs * buffer_length), n_channels))
filter_state = None # for use with the notch filter
decision_buffer = np.zeros((30, 1))
plotter_decision = BCIw.DataPlotter(30, ['Decision'])
# 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:
""" 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]
feature_names = BCIw.get_feature_names(ch_names)
""" 3. INITIALIZE BUFFERS """
# Initialize raw EEG data buffer (for plotting)
eeg_buffer = np.zeros((int(fs * buffer_length), 1))
filter_state = None # for use with the notch filter
# Compute the number of epochs in "buffer_length" (used for plotting)
n_win_test = int(
np.floor((buffer_length - epoch_length) / shift_length + 1))
# Initialize the feature data buffer (for plotting)
feat_buffer = np.zeros((n_win_test, len(feature_names)))
# Initialize the plots
plotter_eeg = BCIw.DataPlotter(fs * buffer_length, ch_names, fs)
plotter_feat = BCIw.DataPlotter(n_win_test, feature_names,
1 / shift_length)
""" 3. GET DATA """
# 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 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,
ch_names = [i-2 for i in indexes_channel]
# Filter
b, a = butter_bandpass(4.0, 35.0, fs, order=4)
#%% Initialize the buffers for storing raw EEG and features
# Initialize raw EEG data buffer (for plotting)
eeg_buffer = np.zeros((fs*eeg_buffer_secs, len(indexes_channel)))
experiment.tone(500,500)
# compute PSD for eeg_buffer
[eeg_buffer_psd, f_ax] = experiment.psd_fft(eeg_buffer, fs)
# Initialize the plots
plotter_time = BCIw.DataPlotter(fs * eeg_buffer_secs, ch_names, fs)
plotter_freq = BCIw.DataPlotter(eeg_buffer_psd.shape[0], ch_names, 1/f_ax[1])
#%% Start pulling data
mules_client.flushdata() # Flush old data from MuLES
# 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:
