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A large EEG dataset for studying cross-session variability in motor imagery brain-computer interface.

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  • 1School of Mechatronic Engineering and Automation, School of Medicine, Research Center of Brain-Computer Engineering, Shanghai University, Shanghai, China.

Scientific Data
|September 1, 2022
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Summary

This study introduces a new dataset for brain-computer interfaces (BCIs) to improve motor imagery (MI) classification across multiple days. Adaptation techniques significantly enhance cross-session accuracy, overcoming EEG signal variability.

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Machine Learning

Background:

  • Brain-computer interfaces (BCIs) face challenges in classifying motor imagery (MI) due to electroencephalography (EEG) signal variability across days.
  • Existing datasets often lack sufficient multi-day data with a large subject pool, hindering robust BCI development.

Purpose of the Study:

  • To introduce a novel, open-access dataset for MI-BCI research, specifically designed to address cross-session and cross-subject variability.
  • To benchmark classification performance under within-session, cross-session, and cross-session adaptation conditions.

Main Methods:

  • Collected a large dataset of left-hand and right-hand MI EEG data from 25 subjects over 5 separate days.
  • Evaluated subject-specific models for within-session (WS), cross-session (CS), and cross-session adaptation (CSA) classification accuracy.

Main Results:

  • Within-session classification (WS) achieved up to 68.8% accuracy.
  • Cross-session classification (CS) accuracy degraded to 53.7% due to session variability.
  • Cross-session adaptation (CSA) significantly improved accuracy to 78.9%.

Conclusions:

  • The new dataset provides a valuable resource for advancing MI-BCI research, particularly for tackling cross-session and cross-subject challenges.
  • Adaptation strategies are crucial for improving the robustness and practical applicability of BCIs in real-world scenarios.