metabci.brainda.datasets.nakanishi2015 module¶
Nakanishi SSVEP dataset.
- class metabci.brainda.datasets.nakanishi2015.Nakanishi2015[source]¶
Bases:
BaseDatasetSSVEP Nakanishi 2015 dataset
This dataset contains 12-class joint frequency-phase modulated steady-state visual evoked potentials (SSVEPs) acquired from 10 subjects used to estimate an online performance of brain-computer interface (BCI) in the reference study [1].
References
“A Comparison Study of Canonical Correlation Analysis Based Methods for Detecting Steady-State Visual Evoked Potentials,” PLoS One, vol.10, no.10, e140703, 2015. http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0140703
- 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)_PATHis 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 Noneforce_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]]]