NeuroTechX / NeuroTechX/moabb

[Dataset] Add datasets from meagmohit EEG-Datasets list

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Description

Dataset Information

Link Status

Working

Description

A curated list of publicly available EEG datasets organized by research paradigm and application domain. Maintained by meagmohit on GitHub as a comprehensive reference for researchers looking for EEG data. Contains approximately 79 distinct EEG datasets.

Relevant Datasets for MOABB

Motor Imagery (11 datasets)
  • Left/Right Hand MI
  • Motor Movement/Imagery Dataset
  • Grasp and Lift EEG Challenge
  • The largest SCP data of Motor-Imagery
  • BCI Competition IV-1, IV-2a, IV-2b
  • High-Gamma Dataset
  • Left/Right Hand 1D/2D movements
  • Imagination of Right-hand Thumb Movement
  • Mental-Imagery Dataset
P300/ERP (8+ datasets)
  • BCI-NER Challenge
  • Target Versus Non-Target (Brain Invaders 2012-2015 series)
  • Pattern Visual Evoked Potentials
SSVEP (5 datasets)
  • c-VEP BCI (wet and dry electrodes)
  • SSVEP - Visual Search/Discrimination and Handshake
  • Synchronized Brainwave Dataset
  • MAMEM EEG SSVEP Datasets (I, II, III)

Notes

⚠️ This is a META-ISSUE pointing to a curated list repository, not a single dataset. Many datasets listed are already implemented in MOABB (BCI Competition, Brain Invaders, MAMEM). Each dataset needs individual evaluation for MOABB inclusion. The list also contains paradigms not currently in MOABB scope (emotion, sleep, error-related potentials).

Related

This issue is a sub-issue of #1 (Discover new datasets)

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start with the linked EEG-Datasets repository and compare its entries with the datasets already implemented in MOABB. No implementation file or test is named; completion would require choosing an in-scope, unimplemented dataset and defining the individual evaluation needed for MOABB inclusion.

Written by the indexing model from the issue text.

Assessment

Tech stack
python
Domain
data
Issue type
Feature
Difficulty
5/5
Estimated time
Over a week
Activity status
Stale
Clarity
Needs clarification
Newbie friendliness
25/100

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