metabci.brainda.datasets.cho2017 module

GigaDb Motor imagery dataset.

class metabci.brainda.datasets.cho2017.Cho2017[source]

Bases: BaseDataset

Motor Imagery dataset from Cho et al 2017.

Dataset from the paper [1].

Dataset Description

We conducted a BCI experiment for motor imagery movement (MI movement) of the left and right hands with 52 subjects (19 females, mean age ± SD age = 24.8 ± 3.86 years); Each subject took part in the same experiment, and subject ID was denoted and indexed as s1, s2, …, s52. Subjects s20 and s33 were both-handed, and the other 50 subjects were right-handed.

EEG data were collected using 64 Ag/AgCl active electrodes. A 64-channel montage based on the international 10-10 system was used to record the EEG signals with 512 Hz sampling rates. The EEG device used in this experiment was the Biosemi ActiveTwo system. The BCI2000 system 3.0.2 was used to collect EEG data and present instructions (left hand or right hand MI). Furthermore, we recorded EMG as well as EEG simultaneously with the same system and sampling rate to check actual hand movements. Two EMG electrodes were attached to the flexor digitorum profundus and extensor digitorum on each arm.

Subjects were asked to imagine the hand movement depending on the instruction given. Five or six runs were performed during the MI experiment. After each run, we calculated the classification accuracy over one run and gave the subject feedback to increase motivation. Between each run, a maximum 4-minute break was given depending on the subject’s demands.

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]]]