Hugging Face's logo

Datasets:
raei
/
Lim-2025-AGTCNetGraphTemporalMotorImageryEEGClassification

Modalities:
Document
Text
Formats:
text
Size:
< 1K
Tags:
neuroscience
electroencephalography
brain-computer-interface
motor-imagery
matlab
bci-competition
Libraries:
Datasets
License:
Dataset card Data Studio Files
xet
 Community
Lim-2025-AGTCNetGraphTemporalMotorImageryEEGClassification / README.md
raei's picture
raei
Add files using upload-large-folder tool
a1c05d0 verified
preview code
|
Raw
History Blame Contribute Delete
3.75 kB
metadata 
license: cc-by-nd-4.0
pretty_name: BCI Competition IV Dataset 2a for AGTCNet
size_categories:
  - 100MB<n<1GB
tags:
  - neuroscience
  - electroencephalography
  - brain-computer-interface
  - motor-imagery
  - matlab
  - bci-competition

BCI Competition IV Dataset 2a

This is an unofficial mirror of the four-class motor-imagery dataset listed by BNCI Horizon 2020 as dataset 001-2014, originally released as BCI Competition IV Dataset 2a. It is provided for reproducing Lim et al.'s AGTCNet experiments and is not affiliated with or endorsed by the dataset contributors, their institutions, BNCI Horizon 2020, the competition organizers, or the AGTCNet authors.

Source and documentation

The 18 scientific MATLAB files and the original description PDF are unchanged from the BNCI Horizon downloads. This dataset card and the checksum manifest are the only added files. The manifest was generated after download and is not an upstream publisher manifest.

The AGTCNet paper also evaluates PhysioNet EEGMMIDB. That dataset is available in the separate raei/Schalk-2004-BCI2000GeneralPurposeBrainComputerInterfaceSystem mirror and is intentionally not duplicated here.

Dataset summary

The dataset contains two sessions recorded on different days from each of nine participants. Every session has 288 cue-based trials, balanced across four motor-imagery classes:

  1. Left hand
  2. Right hand
  3. Both feet
  4. Tongue

Signals comprise 22 EEG and three EOG channels sampled at 250 Hz. They were band-pass filtered from 0.5 to 100 Hz during acquisition, with a 50 Hz notch filter. EOG channels are included for artifact processing and must not be used for classification under the original competition protocol.

Files named A01T.mat through A09T.mat are training sessions; files named A01E.mat through A09E.mat are evaluation sessions. These post-competition MATLAB distributions contain labels for both session types and are the format used by the released AGTCNet loader. Each file contains 288 labels from classes 1 through 4. A04T.mat has a shorter initial EOG block because of a documented technical problem.

Human-participant data

These are human EEG recordings. Users are responsible for following applicable institutional and legal requirements and should not attempt to identify participants.

License

BNCI Horizon 2020 lists the dataset under the Creative Commons Attribution-NoDerivatives 4.0 International license, with the Institute for Knowledge Discovery at Graz University of Technology as licensor. The license permits sharing unchanged copies with appropriate attribution and license notice; modified versions may not be distributed.

Citation

Please cite the dataset description authors and the BCI Competition IV review:

Brunner, C., Leeb, R., Müller-Putz, G. R., Schlögl, A., & Pfurtscheller, G. (2008). BCI Competition 2008 — Graz data set A.

Tangermann, M., Müller, K.-R., Aertsen, A., et al. (2012). Review of the BCI Competition IV. Frontiers in Neuroscience, 6, 55. https://doi.org/10.3389/fnins.2012.00055