metabci.brainda.datasets.bids module

class metabci.brainda.datasets.bids.matchingpennies[source]

Bases: BaseDataset

An example BIDS format dataset. This dataset is an standard example of a BIDS format dataset, that mentioned in [1], and now it can be downloaded from [2]. However, as the suggestion in [3], we download the dataset from BASE_URL instead. The source reference of this dataset is [4].

This is the “Matching Pennies” dataset. It was collected as part of a small scale replication project targeting the following reference [5]

In brief, it contains EEG data for 7 subjects raising either their left or right hand, thus giving rise to a lateralized readiness potential as measured with the EEG. For details, see the Details about the experiment section.

References: [1] Pernet, C.R., Appelhoff, S., Gorgolewski, K.J. et al.

EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Sci Data 6, 103 (2019). https://doi.org/10.1038/s41597-019-0104-8

[2] https://gin.g-node.org/sappelhoff/eeg_matchingpennies [3] https://github.com/mne-tools/mne-bids-pipeline/blob/main/mne_bids_pipeline/tests/datasets.py [4] Appelhoff, S., Sauer, D. & Gill, S. S. Matching Pennies:

A Brain Computer Interface Implementation Dataset. Open Science Framework, https://doi.org/10.17605/OSF.IO/CJ2DR (2018).

[5] Matthias Schultze-Kraft et al. “Predicting Motor Intentions with

Closed-Loop Brain-Computer Interfaces”. In: Springer Briefs in Electrical and Computer Engineering. Springer International Publishing, 2017, pp. 79~90.

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]]]