Datasets

eoglearn.datasets.mne.read_mne_eyetracking_raw(return_events=False, bandpass=True, eyetrack_unit='px')

Return an MNE Raw object containing the EyeLink dataset.

Parameters:

return_eventsbool

If True, return the events for the eyetracking and EEG data.

bandpass: bool

If True, apply a [1, 30]Hz bandpass to the EEG data.

eyetrack_unitstr

The desired unit of the eyetracking data. Must be “px” or “href”, corresponding to pixels-on-screen or head-referenced-eye-angle, respectively. Note that HREF data are reported in radians. If HREF data are requested, a separate data file will be downloaded from OSF, to ~/mne_data/eog-learn-example-data. Defaults to “px”.

Returns:

rawmne.io.Raw

A MNE Raw object containing the EyeLink dataset.

eventsdict

A dictionary where the values for “eyetrack” and “EEG” keys are the events arrays for the eyetracking and EEG data, respectively.

Notes

See MNE-Python’s tutorial for more information on this dataset.

eoglearn.datasets.eegeyenet.fetch_eegeyenet(subject='EP10', run=1, fetch_dataset_kwargs=None)

Fetch a sample file from the EEG Eyenet dataset.

Parameters:

subjectstr

Subject identifier. Defaults to 'EP10'.

runint | str

Which run to download. Most Participants completed 6 runs of the task, saved to 6 different files. Defaults to 1.

fetch_dataset_kwargsdict | None

Keyword arguments to pass to fetch_dataset(). if None, no keyword arguments are passed. Defaults to None.

Returns:

pathlib.Path

Path to the downloaded file.

eoglearn.datasets.eegeyenet.get_subjects_runs(task: Literal['DOTS', 'AS'] = 'DOTS')

Get dictionary of {subject: [lists of runs]}.

Parameters:

task

Which EEGEYENET task task to extract the subject ID’s and runs for. Can be "DOTS", or "AS" (antisaccade). Defaults to 'DOTS'.

Returns:

dict

Dictionary of subjects with the runs as values.