metabci.brainda.datasets.xu2018_minavep module

aVEP datasets

class metabci.brainda.datasets.xu2018_minavep.Xu2018MinaVep(paradigm='aVEP')[source]

Bases: BaseTimeEncodingDataset

Dataset in: M. Xu, X. Xiao, Y. Wang, H. Qi, T. -P. Jung and D. Ming, “A Brain–Computer Interface Based on Miniature-Event-Related Potentials Induced by Very Small Lateral Visual Stimuli,” in IEEE Transactions on Biomedical Engineering, vol. 65, no. 5, pp. 1166-1175, May 2018, doi: 10.1109/TBME.2018.2799661.

This study implemented a miniature aVEP-based BCI speller, and proposed a new scheme for BCI encoding. Thirty-two alphanumeric characters were arranged as a 4 × 8 matrix displayed on a computer screen and encoded by a new SCDMA scheme, in which the left and right lateral visual stimuli constituted two parallel spatial channels while two different lateral visual stimuli sequences made up the basic communication codes ‘0’ and ‘1’. Specifically, the ‘left-right’ stimulus sequence, which lasted 200 ms, was regarded as code ‘0’, while ‘right-left’ stimulus was coded ‘1’. Thirty-two different code sequences were created using 5 bits in this study, which were arbitrarily allocated to different characters. Specifically, character A’ was encoded by ‘01100’. In spelling, the lateral visual stimuli would be presented simultaneously for all characters with different sequences. To obtain a reliable output, the same code sequence was repeated 6 times for the offline spelling and individually optimized times for the online spelling. The character specified to output in the offline spelling would be indicated by a star-shaped cue underneath for 0.8 seconds, which would be offset for another 0.2 seconds to wipe out the cue effect. There was a time interval of 0.2 seconds with no stimulation between two successive sequences.

EEG was recorded using a Neuroscan Synamps2 system with 64 electrodes located in the positions following the 10/20 system. The reference electrode was put in the central area near Cz and the ground electrode was put on the frontal lobe. The recorded signals were bandpass-filtered at 0.1–100 Hz, notch-filtered at 50 Hz, digitized at a rate of 1000 Hz and then stored in a computer.

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]]]