metabci.brainda.datasets.alex_mi module

Alex Motor imagery dataset.

class metabci.brainda.datasets.alex_mi.AlexMI[source]

Bases: BaseDataset

Alex Motor Imagery dataset.

Motor imagery dataset from the PhD dissertation of A. Barachant [1].

This Dataset contains EEG recordings from 8 subjects, performing 2 task of motor imagination (right hand, feet or rest). Data have been recorded at 512Hz with 16 wet electrodes (Fpz, F7, F3, Fz, F4, F8, T7, C3, Cz, C4, T8, P7, P3, Pz, P4, P8) with a g.tec g.USBamp EEG amplifier.

File are provided in MNE raw file format. A stimulation channel encoding the timing of the motor imagination. The start of a trial is encoded as 1, then the actual start of the motor imagination is encoded with 2 for imagination of a right hand movement, 3 for imagination of both feet movement and 4 with a rest trial.

The duration of each trial is 3 second. There is 20 trial of each class.

References

data_path(subject: str | int, path: str | Path | None = None, force_update: bool = False, update_path: bool | None = None, proxies: Dict[str, str] | None = None, verbose: bool | str | int | None = None) → List[List[str | Path]][source]

Get path to local copy of a subject data.

Parameters:

  • subject (Union[str, int]) – subject id

  • path (Optional[Union[str, Path]], optional) – Location of where to look for the data storing location. If None, the environment variable or config parameter MNE_DATASETS_(dataset_code)_PATH is used. If it doesn’t exist, the “~/mne_data” directory is used. If the dataset is not found under the given path, the data will be automatically downloaded to the specified folder, by default None

  • force_update (bool, optional) – force update of the dataset even if a local copy exists, by default False

  • update_path (Optional[bool], optional) – If True, set the MNE_DATASETS_(dataset)_PATH in mne-python config to the given path. If None, the user is prompted, by default None

  • proxies (Optional[Union[bool, str, int]], optional) – proxies if needed

  • verbose (Optional[Union[bool, str, int]], optional) – [description], by default None

Returns:

local path of a subject data, the first list is session and the second list is run

Return type:

List[List[Union[str, Path]]]