#%% Initialize the buffers for storing raw EEG and features
# Initialize raw EEG data buffer (for plotting)
eeg_buffer = np.zeros((params['sampling frequency']*eeg_buffer_secs,
len(params['names of channels'])))
# Compute the number of windows in "eeg_buffer_secs" (used for plotting)
n_win_test = int(np.floor((eeg_buffer_secs - win_test_secs) / float(shift_secs) + 1))
# Initialize the feature data buffer (for plotting)
feat_buffer = np.zeros((n_win_test, len(names_of_features)))
# Initialize the plots
plotter_eeg = BCIw.dataPlotter(params['sampling frequency']*eeg_buffer_secs,
params['names of channels'],
params['sampling frequency'])
plotter_feat = BCIw.dataPlotter(n_win_test,
names_of_features,
1/float(shift_secs))
#%% Start pulling data
mules_client.flushdata() # Flush old data from MuLES
BCIw.beep() # Beep sound
# The try/except structure allows to quit the while loop by aborting the
# script with <Ctrl-C>
feat_matrix1 = BCIw.compute_feature_matrix(eeg_epochs1, params['sampling frequency'])
#%% Train classifier
[classifier, mu_ft, std_ft] = BCIw.classifier_train(feat_matrix0, feat_matrix1, 'SVM')
BCIw.beep(500,300)
#%% Initialize the buffers for storing raw EEG and decisions
eeg_buffer = np.zeros((params['sampling frequency']*eeg_buffer_secs, len(params['data format'])))
decision_buffer = np.zeros((30,1))
mules_client.flushdata() # Flushes old data from MuLES
plotter_decision = BCIw.dataPlotter(30, ['Decision'])
#BCIw.plot_classifier_training(feat_matrix0, feat_matrix1) # This will plot the decision boundary of a 2-feature SVM
#%% Start pulling data and classifying in real-time
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:
#%% Train classifier
[classifier, mu_ft,
std_ft] = BCIw.classifier_train(feat_matrix0, feat_matrix1, 'SVM')
BCIw.beep(500, 300)
#%% Initialize the buffers for storing raw EEG and decisions
eeg_buffer = np.zeros((params['sampling frequency'] * eeg_buffer_secs,
len(params['data format'])))
decision_buffer = np.zeros((30, 1))
mules_client.flushdata() # Flushes old data from MuLES
plotter_decision = BCIw.dataPlotter(30, ['Decision'])
#BCIw.plot_classifier_training(feat_matrix0, feat_matrix1) # This will plot the decision boundary of a 2-feature SVM
#%% Start pulling data and classifying in real-time
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(
#%% Initialize the buffers for storing raw EEG and features
# Initialize raw EEG data buffer (for plotting)
eeg_buffer = np.zeros((params['sampling frequency']*eeg_buffer_secs, len(params['names of channels'])))
# Compute the number of windows in "eeg_buffer_secs" (used for plotting)
n_win_test = int(np.floor((eeg_buffer_secs - win_test_secs) / float(shift_secs) + 1))
# Initialize the feature data buffer (for plotting)
feat_buffer = np.zeros((n_win_test, len(names_of_features)))
# Initialize the plots
plotter_eeg = BCIw.dataPlotter(params['sampling frequency']*eeg_buffer_secs,
params['names of channels'],
params['sampling frequency'])
plotter_feat = BCIw.dataPlotter(n_win_test,
names_of_features,
1 / float(shift_secs))
#%% Start pulling data
mules_client.flushdata() # Flush old data from MuLES
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')