Research on EEG dataset construction and partitioning strategies for motor imagery decoding

Qiuyuan Qi; Roumin Tong; Yang Yu

doi:10.1117/12.3049400

27 September 2024 Research on EEG dataset construction and partitioning strategies for motor imagery decoding

Qiuyuan Qi, Roumin Tong, Yang Yu

Proceedings Volume 13284, Third International Conference on Intelligent Mechanical and Human-Computer Interaction Technology (IHCIT 2024); 132841M (2024) https://doi.org/10.1117/12.3049400
Event: Third International Conference on Intelligent Mechanical and Human-Computer Interaction Technology (IHCIT 2024), 2024, Hangzhou, China

Abstract

In the field of Brain-Computer Interfaces (BCI), while research on Motor Imagery (MI) decoding models has advanced significantly, systematic studies on dataset construction and partitioning strategies remain scarce. Inappropriate data partitioning may lead to models performing well in experimental settings but underperforming in real-world applications. This study compares the impact of various EEG dataset construction and partitioning strategies on MI decoding model performance to optimize BCI systems. Utilizing the BCI Competition IV 2a dataset, we designed three data construction strategies: Intra-session training and testing, Cross-session evaluation, and Combined session evaluation. We applied four data partitioning strategies: holdout, cross-validation, shuffled cross-validation, and time series cross-validation. For model architecture, we employed the end-to-end deep learning framework EEGNet. Results indicate that increasing data volume enhances model performance. Among partitioning strategies, shuffled cross-validation showed superior performance in improving model accuracy. The holdout method also performed well with sufficient data, offering a computationally efficient alternative. These findings provide valuable insights for optimizing BCI system development and evaluation methodologies.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qiuyuan Qi, Roumin Tong, and Yang Yu "Research on EEG dataset construction and partitioning strategies for motor imagery decoding", Proc. SPIE 13284, Third International Conference on Intelligent Mechanical and Human-Computer Interaction Technology (IHCIT 2024), 132841M (27 September 2024); https://doi.org/10.1117/12.3049400

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KEYWORDS

Data modeling

Cross validation

Performance modeling

Electroencephalography

Education and training

Brain-machine interfaces

Deep learning

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