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Merged CNNs for the classification of EEG motor imagery signals

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Abstract

The using of Electroencephalography (EEG) signals for motor imagery (MI) has recently gained significant attention due to their remarkable ability to detect an individual’s intention to perform specific actions. MI signals have proven useful in enabling individuals with disabilities to control devices such as wheelchairs through neural commands, and have even expanded into applications like autonomous driving. Therefore, ensuring accurate classification of MI tasks from EEG signals is crucial for the development of a reliable Brain-Computer Interface (BCI) system. This article introduces a novel approach to classifying MI tasks using Deep Learning (DL) techniques. The proposed methodology encompasses several steps, including data preprocessing, feature extraction using Common Spatial Pattern (CSP) and Wavelet Packet Decomposition (WPD), and the evaluation of four distinct classifiers. These classifiers involve combinations of two, three, four, and five Convolutional Neural Networks (CNNs). Empirical evaluations highlight the effectiveness of employing five CNNs, which yield the most favorable results. Our approach demonstrates promising performance metrics such as accuracy, precision, recall, and F1 score. Specifically, the method achieves accuracy, precision, recall, and F1 score values of 64.75%, 64.94%, 65.63%, and 64.13%, respectively, indicating its potential to enhance the accuracy of MI task classification within EEG signals.

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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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Acknowledgements

This research work was funded by Institutional Fund Projects under grant no. (IFPIP: 1137-135-1443). The authors gratefully acknowledge technical and financial support provided by the Ministry of Education and King Abdelaziz University, DSR, Jeddah, Saudi Arabia.

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

    Amira Echtioui & Mohamed Ghorbel

  2. King Abdulaziz University (KAU), Jeddah, Saudi Arabia

    Wassim Zouch

Authors
  1. Amira Echtioui
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  2. Wassim Zouch
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  3. Mohamed Ghorbel
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Contributions

Conceptualization was done by AE. All the literature reading and data gathering were performed by AE. All the experiments and coding were performed by AE. The formal analysis was performed by AE. Manuscript writing original draft preparation was done by AE, WZ, MG. Review and editing was done by AE, WZ, MG. Visualization work was carried out by AE, WZ, MG.

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Correspondence to Amira Echtioui.

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Echtioui, A., Zouch, W. & Ghorbel, M. Merged CNNs for the classification of EEG motor imagery signals. Multimed Tools Appl 84, 373–395 (2025). https://doi.org/10.1007/s11042-024-18892-8

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  • DOI: https://doi.org/10.1007/s11042-024-18892-8

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