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Multi-class Classification of Motor Imagery EEG Signals Using Deep Learning Models

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Abstract

The accurate classification of Motor Imagery (MI) electroencephalography (EEG) signals is crucial for advancing Brain-Computer Interface (BCI) technologies, particularly for individuals with disabilities. In this study, we present a sophisticated deep learning methodology that systematically evaluates three models CNN, RNN, and BiLSTM, to identify the optimal approach for MI signal classification. Leveraging the BCI Competition IV 2a dataset, we applied a pre-processing step removing three EOG channels and retaining 22 EEG channels, extracting 288 MI epochs, each lasting 3 s. Our findings highlight the superior performance of the proposed RNN model, achieving a remarkable maximum accuracy of 98%. This outcome signifies a significant advancement in MI signal classification, demonstrating the potential of deep learning techniques to enhance BCI precision. The study contributes by introducing a novel methodology and showcasing its efficacy through rigorous evaluation against benchmarks, providing valuable insights for the development of more robust and accurate BCI systems.

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Data Availability

The dataset used in this study is public and can be found at the following links: https://www.bbci.de/competition/iv/.

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Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

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Authors and Affiliations

  1. Advanced Technologies for Medicine and Signals Laboratory ‘ATMS’, National Engineering School of Sfax (ENIS), Sfax University, Sfax, Tunisia

    Rafik Khemakhem, Sana Belgacem, Amira Echtioui & Mohamed Ghorbel

  2. Higher Institute of Management of Gabès, Gabès University, Gabès, Tunisia

    Rafik Khemakhem

  3. Department IS, College of Computer Science, King Khaled University ‘KKU’, Abha, Kingdom of Saudi Arabia

    Ahmed Ben Hamida

  4. Functional Exploration Department of Habib Bourguiba Hospital, Sfax University, Sfax, Tunisia

    Ines Kammoun

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  1. Rafik Khemakhem
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  2. Sana Belgacem
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  3. Amira Echtioui
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  4. Mohamed Ghorbel
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  5. Ahmed Ben Hamida
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  6. Ines Kammoun
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Correspondence to Rafik Khemakhem.

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Khemakhem, R., Belgacem, S., Echtioui, A. et al. Multi-class Classification of Motor Imagery EEG Signals Using Deep Learning Models. SN COMPUT. SCI. 5, 444 (2024). https://doi.org/10.1007/s42979-024-02845-x

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