NeuroTechX / NeuroTechX/moabb

[Dataset] Add datasets from OpenLists ElectrophysiologyData repository

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Description

Dataset Information

Description

A comprehensive, community-maintained GitHub repository that curates openly available electrophysiology datasets. The repository organizes datasets by category (large-scale projects, BCI-focused, cognitive tasks, movement/motor, sleep, clinical populations, etc.) and provides direct links to data repositories including OpenNeuro, PhysioNet, Figshare, OSF, and GigaDB.

This is a valuable discovery resource for identifying new datasets to add to MOABB.


Datasets Already in MOABB

  • ERP CORE - All 7 paradigms implemented (ErpCore2021_ERN, ErpCore2021_LRP, ErpCore2021_MMN, ErpCore2021_N170, ErpCore2021_N2pc, ErpCore2021_N400, ErpCore2021_P3)
  • BNCI Horizon 2020 - Multiple datasets implemented (see BNCI issues #919-#935)
  • PhysioNet Motor Movement/Imagery - 109 subjects (PhysionetMI)
  • GigaDB Motor Imagery (Cho2017) - 52 subjects (Cho2017)
  • Grasp and Lift - 12 subjects (related to #942 Luciw2014)

BCI-Relevant Datasets to Add (High Priority)

Motor Imagery
  • EEG/BCI Sensorimotor Longitudinal - 62 subjects, longitudinal MI data (Figshare) - See #940
  • Mental Imagery for BCI - 17 subjects, 4 mental imagery tasks (Figshare)
  • EEG BCI Mental Imagery Multi-Session - 13 subjects (Figshare)
SSVEP
  • Multi-day SSVEP - 30 subjects (GigaDB)
P300/ERP
  • Nencki-Symfonia EEG/ERP - 42 subjects, high-density EEG, oddball paradigm (GigaDB)
  • Visual Oddball Task - 18 subjects (NITRC)
Multi-Paradigm
  • An EEG/BCI Multi-Paradigm Dataset - 54 subjects, multiple paradigms & sessions (GigaDB)
  • Mobile BCI (Scalp & Ear EEG) - 24 subjects, ERP & SSVEP while moving (OSF) - See #877
Inner Speech / Covert
  • EEG/BCI Inner Speech Recognition - 10 subjects (OpenNeuro) - See #876

Cognitive Task Datasets (Medium Priority)

  • EEG Working Memory Task (ERP) - 104 subjects (OSF)
  • Visual Attention Task (Covert) - 50 subjects (OSF)
  • Large EEG Dataset - Gambling Task - 500 subjects (OSF)
  • RSVP Task - 50 subjects (OpenNeuro) - See #900

Movement/Motor Execution Datasets

  • EEG Motion Capture Treadmill Walking - 8 subjects (Figshare)
  • EEG During Perturbed Walking & Standing - 30 subjects (Data in Brief)
  • Post-Stroke Arm Motion Dataset - 45 subjects with EEG (Harvard Dataverse)

Large-Scale / Resting State (Lower BCI Priority)

  • HBN (Healthy Brain Networks) - ~1000 subjects (developmental focus)
  • LEMON - 228 subjects (resting state, multimodal)
  • TUH EEG Corpus - 30,000+ clinical recordings
  • TDBRAIN - 1,200 subjects clinical EEG
  • Cuban Human Brain Mapping - 282 subjects

Datasets with Existing Issues

Dataset Issue
Inner Speech #876
Mobile BCI #877
RSVP #900
Grasp and Lift (Luciw2014) #942
Walking datasets #943, #944
Longitudinal MI (Zhou2020) #940

Data Repositories to Monitor

The OpenLists repository also links to these data repositories which should be periodically checked for new BCI-relevant datasets:

  • OpenNeuro - BIDS-formatted neuroimaging
  • PhysioNet - Physiological data
  • Figshare - General scientific data
  • OSF - Open Science Framework
  • GigaDB - Genomics/neuroscience data
  • Zenodo - Research data repository

Notes

This is a META-ISSUE for tracking datasets from the OpenLists repository. Individual datasets should have their own issues created when someone begins implementation work.

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 OpenLists ElectrophysiologyData repository and the dataset checklist in this issue, then review the linked existing issues and parent issue #1. This is a meta-issue rather than a single implementation task; done means selecting an individual dataset and creating or working from a dedicated implementation issue.

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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