src/data/create_data.py
def eeg_epochs_dataset(subject, trial, config, preload=True):
    """Get the epoched eeg data excluding unnessary channels
    from fif file and also filter the signal.

    Parameter
    ----------
    subject : string of subject ID e.g. 7707
    trial   : HighFine, HighGross, LowFine, LowGross

    Returns
    ----------
    epochs  : epoched data

    """
    epoch_length = config['epoch_length']
    overlap = config['overlap']

    read_path = config['raw_data']['raw_eeg_path']
    raw_eeg = dd.io.load(read_path, group='/' + subject + '/eeg/' + trial)

    events = mne.make_fixed_length_events(raw_eeg,
                                          duration=epoch_length,
                                          overlap=epoch_length * overlap)
    epochs = mne.Epochs(raw_eeg,
                        events,
                        tmin=0,
                        tmax=config['epoch_length'],
                        verbose=False,
                        baseline=(0, 0),
                        preload=preload)

    return epochs
Get the epoched eeg data excluding unnessary channels
    from fif file and also filter the signal.

    Parameter
    ----------
    subject : string of subject ID e.g. 7707
    trial   : HighFine, HighGross, LowFine, LowGross

    Returns
    ----------
    epochs  : epoched data

    
Get the epoched eeg data excluding unnessary channels
from fif file and also filter the signal.

Parameter

subject : string of subject ID e.g.

Returns

epochs  : epoched data
Get the epoched eeg data excluding unnessary channels from fif file and also filter the signal . Parameter subject : string of subject ID e . g . Returns epochs : epoched data
def eeg_epochs_dataset(subject, trial, config, preload=True):
    epoch_length = config['epoch_length']
    overlap = config['overlap']
    read_path = config['raw_data']['raw_eeg_path']
    raw_eeg = dd.io.load(read_path, group='/' + subject + '/eeg/' + trial)
    events = mne.make_fixed_length_events(raw_eeg,
                                          duration=epoch_length,
                                          overlap=epoch_length * overlap)
    epochs = mne.Epochs(raw_eeg,
                        events,
                        tmin=0,
                        tmax=config['epoch_length'],
                        verbose=False,
                        baseline=(0, 0),
                        preload=preload)
    return epochs
def eeg_epochs_dataset ( subject , trial , config , preload = True ) : epoch_length = config [ 'epoch_length' ] overlap = config [ 'overlap' ] read_path = config [ 'raw_data' ] [ 'raw_eeg_path' ] raw_eeg = dd . io . load ( read_path , group = '/' + subject + '/eeg/' + trial ) events = mne . make_fixed_length_events ( raw_eeg , duration = epoch_length , overlap = epoch_length * overlap ) epochs = mne . Epochs ( raw_eeg , events , tmin = 0 , tmax = config [ 'epoch_length' ] , verbose = False , baseline = ( 0 , 0 ) , preload = preload ) return epochs
Get the epoched eeg data excluding unnessary channels
from fif file and also filter the signal.
Get the epoched eeg data excluding unnessary channels from fif file and also filter the signal .
"""Get the epoched eeg data excluding unnessary channels
    from fif file and also filter the signal.

    Parameter
    ----------
    subject : string of subject ID e.g. 7707
    trial   : HighFine, HighGross, LowFine, LowGross

    Returns
    ----------
    epochs  : epoched data

    """
subject
trial
config
preload