moabb.datasets.Hinss2021#

class moabb.datasets.Hinss2021(subjects=None, sessions=None)[source]#

Neuroergonomic 2021 dataset.

Dataset summary

#Subj

15

#Chan

62

#Classes

4

Trials length

2 s

Freq

250 Hz

#Sessions

1

#Blocks / class

1

Participants

  • Population: healthy

  • Age: 23.9 years

  • Handedness: {‘left’: 2, ‘right’: 27}

Equipment

  • Amplifier: Brain Products

  • Electrodes: active Ag-AgCl

  • Montage: 10-20

  • Reference: Car

Preprocessing

  • Data state: raw

  • Re-reference: car

Data Access

Experimental Protocol

  • Paradigm: rstate

  • Feedback: trial-based feedback (Flanker task provides correct/incorrect/miss feedback)

  • Stimulus: avatar

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We describe the experimental procedures for a dataset that is publicly available at https://zenodo.org/records/5055046. This dataset contains electroencephalographic recordings of 15 subjects (6 female, with an average age of 25 years). A total of 62 active Ag–AgCl electrodes were available in the dataset.

The participants engaged in 3 (2 available here) distinct experimental sessions, each of which was separated by 1 week.

At the beginning of each session, the resting state of the participant (measured as 1 minute with eyes open) was recorded.

Subsequently, participants undertook 3 tasks of varying difficulty levels (i.e., easy, medium, and difficult). The task assignments were randomized. For more details, please check [Hinss2021].

Notes

Added in version 1.0.1.

References

[Hinss2021]

M. Hinss, B. Somon, F. Dehais & R. N. Roy (2021) Open EEG Datasets for Passive Brain-Computer Interface Applications: Lacks and Perspectives. IEEE Neural Engineering Conference.

[Hinss2023]

M. F. Hinss, et al. (2023) An EEG dataset for cross-session mental workload estimation: Passive BCI competition of the Neuroergonomics Conference 2021. Scientific Data, 10, 85. https://doi.org/10.1038/s41597-022-01898-y

data_path(subject, 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)_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.

  • 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:

list of str

Examples using moabb.datasets.Hinss2021#

Dataset bubble plot

Dataset bubble plot

Hinss2021 classification example

Hinss2021 classification example