[Dataset] Add Kim2018 Motor Imagery dataset (wheelchair control)
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Description
Dataset Information
| Field | Value |
|---|---|
| Name | Kim2018 |
| Subjects | 12 |
| Channels | 30 |
| Sampling Rate | 250 Hz |
| Paradigm | Motor Imagery |
| Tasks | Left hand, Right hand, Right foot |
| Publication | IEEE TNSRE, 2018 |
| DOI | 10.1109/TNSRE.2017.2778113 |
| Repository | Deep BCI |
Description
Motor imagery dataset designed for wheelchair control BCI applications.
Source
Identified from Gwon et al. 2023 review paper (DOI: 10.3389/fnhum.2023.1134869)
Related
This issue is a sub-issue of #1 (Discover new datasets)
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
No implementation file or test is named. Start by locating existing dataset integrations and compare how the Kim2018 metadata, motor-imagery tasks, and Deep BCI source are represented; verify the DOI and dataset details before adding it. Done means the dataset is available through MOABB with its 12 subjects, 30 channels, 250 Hz sampling rate, and three listed tasks.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- data, machine-learning
- Issue type
- Feature
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 45/100