metabci.brainda.datasets.cattan_P300 module

P300 datasets

class metabci.brainda.datasets.cattan_P300.Cattan_P300(paradigm='p300')[source]

Bases: BaseTimeEncodingDataset

Dataset in: Grégoire Cattan, Anton Andreev, Pedro Luiz Coelho Rodrigues and Marco Congedo, “Dataset of an EEG-based BCI experiment in Virtual Reality and on a Personal Computer,” in arXiv.1903.11297.1903.11297, 2019,

This dataset contains electroencephalographic recordings on 21 subjects doing a visual P300 experiment on PC (personal computer) and VR (virtual reality). The visual P300 is an event-related potential elicited by a visual stimulation, peaking 240-600 ms after stimulus onset. The experiment was designed in order to compare the use of a P300-based brain-computer interface on a PC and with a virtual reality headset, concerning the physiological, subjective and performance aspects. The brain-computer interface is based on electroencephalography (EEG). EEG data were recorded thanks to 16 electrodes. The virtual reality headset consisted of a passive head-mounted display, that is, a head-mounted display which does not include any electronics with the exception of a smartphone. A full description of the experiment is available at https://hal.archives-ouvertes.fr/hal-02078533. This experiment was carried out at GIPSA-lab (University of Grenoble Alpes, CNRS, Grenoble-INP) in 2018, and promoted by the IHMTEK Company (Interaction Homme-Machine Technologie). The study was approved by the Ethical Committee of the University of Grenoble Alpes (Comité d’Ethique pour la Recherche Non-Interventionnelle). The ID of this dataset is VR.EEG.2018-GIPSA.

This study implemented a P300 interface. Thirty-six characters were arranged as a 6 × 6 matrix displayed on the screen. The task of the subject was to focus on one of the characters.The experiment was composed of two sessions. One session ran under the PC condition and the other under the VR condition. The order of the session was randomized for all subjects. Each session comprised 12 blocks of five repetitions. All the repetitions within a block have the same target. A repetition consisted of 12 flashes of groups of six symbols chosen in such a way that after each repetition each symbol has flashed exactly two times. Thus, in each repetition the target symbol flashes twice, whereas the remaining ten flashes do not concern the target (non-target). The EEG signal was tagged corresponding to each flash.

The recorded signals were bandpass-filtered at 0.1–100 Hz, notch-filtered at 50 Hz, digitized at a rate of 500 Hz and then stored in a computer. In this script, data is downscaled to 100 Hz.

code_len = 12
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)[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]]]