def run_experiment():
eeg = EEG(board_id=2, ip_port=6677, serial_port="COM4")
exp = OnlineExperiment(eeg=eeg, model=None, num_trials=3, buffer_time=3, threshold=3)
exp.run(use_eeg=False)
def main():
eeg = EEG(board_id=2, ip_port=6677, serial_port="COM6")
exp = OfflineExperiment(eeg=eeg, num_trials=4, trial_length=2)
trials, labels = exp.run()
trials = preprocess(eeg, trials, ch_names=['C3', 'C4'])
features = extract_features(eeg,
trials,
features=['ptp_amp', 'mean', 'skewness'])
# features, labels = load_featuresNlabels_fromPKL()
model, mean_acc = train_model(features, labels)
print(mean_acc)
def run_experiment(model_path: str):
model = pickle.load(open(model_path, 'rb'))
SYNTHETIC_BOARD = -1
CYTON_DAISY = 2
eeg = EEG(board_id=SYNTHETIC_BOARD)
exp = OnlineExperiment(eeg=eeg,
model=model,
num_trials=10,
buffer_time=4,
threshold=3,
skip_after=8,
co_learning=True,
debug=False)
exp.run(use_eeg=True, full_screen=True)
def offline_experiment():
SYNTHETIC_BOARD = -1
CYTON_DAISY = 2
eeg = EEG(board_id=CYTON_DAISY)
exp = OfflineExperiment(eeg=eeg,
num_trials=20,
trial_length=5,
full_screen=True,
audio=False)
trials, labels = exp.run()
# Classification
model = MLModel(trials=trials, labels=labels)
session_directory = exp.session_directory
model.offline_training(eeg=eeg, model_type='csp_lda')
# Dump the MLModel
pickle.dump(model,
open(os.path.join(session_directory, 'model.pickle'), 'wb'))
def control_mouse(config: MouseConfig, model_path: str):
# Init variables
model = MLModel(model_path=model_path)
eeg = EEG(board_id=-1)
vm = VirtualMouse(eeg=eeg, model=model, mouse_actions=config.mouse_actions)
# Turn EEG on
eeg.on()
# Start controlling the virtual mouse
while True:
# Monitor the movement to indicate standstill
vm.monitor(r=25, counter_limit=5, interval=0.4)
# Predict the label imagined by the user
label = vm.predict(buffer_time=4)
# Convert the label to action according to current config
action = config.get_action(label=label)
# Execute the action
vm.execute(action=action)