moabb.datasets.DemonsP300#
- class moabb.datasets.DemonsP300[source]#
Visual P300 dataset recorded in Virtual Reality (VR) game Raccoons versus Demons.
Dataset summary
#Subj
#Chan
#Trials / class
Trials length
Sampling rate
#Sessions
60
8
935 NT / 50 T
1s
500Hz
1
Danger
This dataset contains major unresolved issues and could removed in the near futur. Use it in a benchmark at your own risk. See NeuroTechX/moabb#216
Dataset Description
We publish dataset of visual P300 BCI performed in Virtual Reality (VR) game Raccoons versus Demons (RvD). Data contains reach labels incorporating information about stimulus chosen enabling us to estimate model’s confidence at each stimulus prediction stage. target channel contains standard P300 target/non-target labels, while mult_target channel contains multiclass labels (numbers of activated stimuli).
Participants
60 healthy participants (23 males) naive to BCI with mean age 28 years from 19 to 45 y.o. took part in the study. All subject signed informed consent and passed primary prerequisites on their health and condition.
Stimulation and EEG recording
The EEG was recorded with NVX-52 encephalograph (MCS, Zelenograd, Russia) at 500 Hz. We used 8 sponge electrodes (Cz, P3, P4, PO3, POz, PO4, O1, O2). Stimuli were presented with HTC Vive Pro VR headset with TTL hardware sync
Experimental procedure
Participants were asked to play the P300 BCI game in virtual reality. BCI was embedded into a game plot with the player posing as a forest warden. The player was supposed to feed animals and protect them from demons. Game mechanics consisted in demons jumping (visually activating), so player have to concentrate on one demon (chosen freely). That produced P300 response in time of the daemon jump. That was the way to trigger fireball towards a daemon predicted by classifier from EEG data.
More info can be found in [1] [2] [3].
References
- 1
Goncharenko V., Grigoryan R., and Samokhina A. (May 12, 2020), Raccoons vs Demons: multiclass labeled P300 dataset, https://arxiv.org/abs/2005.02251
- 2
Goncharenko V., Grigoryan R., and Samokhina A., Approaches to multiclass classification of P300 potential datasets, Intelligent Data Processing: Theory and Applications:Book of abstract of the 13th International Conference, Moscow, 2020. — Moscow: Russian Academy of Sciences, 2020. — 472 p.ISBN 978-5-907366-16-9 http://www.machinelearning.ru/wiki/images/3/31/Idp20.pdf
- 3
Goncharenko V., Grigoryan R., and Samokhina A., P300 potentials dataset and approaches to its processing, Труды 63-й Всероссийской научной конференции МФТИ. 23–29 ноября 2020 года. Прикладные математика и информатика. — Москва : МФТИ, 2020. – 334 с. ISBN 978-5-7417-0757-9 https://mipt.ru/science/5top100/education/courseproposal/%D0%A4%D0%9F%D0%9C%D0%98%20%D1%84%D0%B8%D0%BD%D0%B0%D0%BB-compressed2.pdf
- data_path(subject: int, path=None, force_update=False, update_path=None, verbose=None)[source]#
Get path to local copy of a subject data.
- Parameters
subject (int) – Number of subject to use
path (None | str) – Location of where to look for the data storing location. If None, the environment variable or config parameter
MNE_DATASETS_(dataset)_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.force_update (bool) – Force update of the dataset even if a local copy exists.
update_path (bool | None Deprecated) – If True, set the MNE_DATASETS_(dataset)_PATH in mne-python config to the given path. If None, the user is prompted.
verbose (bool, str, int, or None) – If not None, override default verbose level (see
mne.verbose()).
- Returns
path – Local path to the given data file. This path is contained inside a list of length one, for compatibility.
- Return type