# Creates a mules_client
mules_client = mules.MulesClient(mules_ip, muse_port)
params = mules_client.getparams() # Get the device parameters
#%% Set the experiment parameters
eeg_buffer_secs = 15 # Size of the EEG data buffer used for plotting the
# signal (in seconds)
win_test_secs = 1 # Length of the window used for computing the features
# (in seconds)
overlap_secs = 0.5 # Overlap between two consecutive windows (in seconds)
shift_secs = win_test_secs - overlap_secs
index_channel = 1 # Index of the channnel to be used (with the Muse, we
# can choose from 0 to 3)
# Get name of features
names_of_features = BCIw.feature_names(params["names of channels"])
#%% 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"]
#%% Set the experiment parameters
eeg_buffer_secs = 15 # Size of the EEG data buffer used for plotting the
# signal (in seconds)
win_test_secs = 1 # Length of the window used for computing the features
# (in seconds)
overlap_secs = 0.5 # Overlap between two consecutive windows (in seconds)
shift_secs = win_test_secs - overlap_secs
index_channel = 1 # Index of the channnel to be used (with the Muse, we
# can choose from 0 to 3)
# This line changes params to work with only one electrode
params['names of channels'] = ['CH1','STATUS']
# Get name of features
names_of_features = BCIw.feature_names(params['names of channels'])
#%% 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
# Creates a mules_client
mules_client = mules.MulesClient(mules_ip, muse_port)
params = mules_client.getparams() # Get the device parameters
#%% Set the experiment parameters
eeg_buffer_secs = 15 # Size of the EEG data buffer used for plotting the
# signal (in seconds)
win_test_secs = 1 # Length of the window used for computing the features
# (in seconds)
overlap_secs = 0.5 # Overlap between two consecutive windows (in seconds)
shift_secs = win_test_secs - overlap_secs
index_channel = 1 # Index of the channnel to be used (with the Muse, we
# can choose from 0 to 3)
# Get name of features
names_of_features = BCIw.feature_names(params['names of channels'])
#%% 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,