# Record data for mental activity 1
BCIw.beep() # Beep sound
eeg_data1, timestamps1 = inlet.pull_chunk(timeout=training_length + 1,
max_samples=fs * training_length)
eeg_data1 = np.array(eeg_data1)[:, index_channel]
# Divide data into epochs
eeg_epochs0 = BCIw.epoch(eeg_data0, epoch_length * fs, overlap_length * fs)
eeg_epochs1 = BCIw.epoch(eeg_data1, epoch_length * fs, overlap_length * fs)
""" 4. COMPUTE FEATURES AND TRAIN CLASSIFIER """
feat_matrix0 = BCIw.compute_feature_matrix(eeg_epochs0, fs)
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>
eeg_data1, timestamps1 = inlet.pull_chunk(
timeout=training_length+1, max_samples=fs * training_length)
eeg_data1 = np.array(eeg_data1)[:, index_channel]
# Divide data into epochs
eeg_epochs0 = BCIw.epoch(eeg_data0, epoch_length * fs,
overlap_length * fs)
eeg_epochs1 = BCIw.epoch(eeg_data1, epoch_length * fs,
overlap_length * fs)
""" 4. COMPUTE FEATURES AND TRAIN CLASSIFIER """
feat_matrix0 = BCIw.compute_feature_matrix(eeg_epochs0, fs)
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
eeg_data1, timestamps1 = inlet.pull_chunk(
timeout=training_length+1, max_samples=fs * training_length)
eeg_data1 = np.array(eeg_data1)[:, index_channel]
# Divide data into epochs
eeg_epochs0 = BCIw.epoch(eeg_data0, epoch_length * fs,
overlap_length * fs)
eeg_epochs1 = BCIw.epoch(eeg_data1, epoch_length * fs,
overlap_length * fs)
""" 4. COMPUTE FEATURES AND TRAIN CLASSIFIER """
feat_matrix0 = BCIw.compute_feature_matrix(eeg_epochs0, fs)
feat_matrix1 = BCIw.compute_feature_matrix(eeg_epochs1, fs)
[classifier, mu_ft, std_ft] = BCIw.train_classifier(
feat_matrix0, feat_matrix1, 'SVM')
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.')