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A novel precisely designed compact convolutional EEG classifier for motor imagery classification

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Abstract

Robust classification of electroencephalogram data for motor imagery recognition is of paramount importance in brain–computer interface (BCI) domain. Since EEG signals are highly subject-dependent, inter-subject variations can greatly impair the robustness of motor imagery (MI) classification. Therefore, this study introduces a precisely designed deep learning architecture namely compact convolutional EEG classifier (CCEC) which achieves better performance in both precision and efficiency. Specifically, the recorded EEG signals are first denoised using multiscale principal component analysis (MSPCA) technique. Then, such raw EEG data are converted into small tempo-spatial data matrices with a two-step signal preprocessing technique. Finally, the tempo-spatial matrices are fed to the proposed CCEC model for MI classification. Experimental results on two benchmark datasets demonstrate that the proposed model not only performs exceptionally well in subject-specific case with an average classification accuracy of 98.2% on dataset 1 but also shows a reasonable average classification accuracy of 72.64% in the subject-independent case. Additionally, with a mere 10% adaptation to subject-specific data, a further improvement of 18% is achieved, thus attaining a noteworthy 90% accuracy in the inter-subject classification. Results also reveal that the proposed CCEC model is highly robust to noisy data, ensuring reliable performance in real-world scenarios.

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Availability of data and materials

The dataset underlying the results presented in this paper could be obtained from corresponding author upon reasonable request, and the relevant code of the experiment will be published at https://github.com/

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Funding

This research is supported in part by the higher education research fund (International Talent Training Special) (GJGZMS202201), the postgraduate education research fund (2022AJ13), and education & teaching reform research project (2022JGZ14) of Northwestern Polytechnical University.

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Author notes

  1. Muhammad Ahmed Abbasi and Hafza Faiza Abbasi: co first authors.

Authors and Affiliations

  1. School of Automation, Northwestern Polytechnical University, Xi’an, 710072, Shaanxi, P R China

    Muhammad Ahmed Abbasi, Hafza Faiza Abbasi, Muhammad Zulkifal Aziz, Waseem Haider, Zeming Fan & Xiaojun Yu

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  1. Muhammad Ahmed Abbasi
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  2. Hafza Faiza Abbasi
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  3. Muhammad Zulkifal Aziz
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  4. Waseem Haider
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  5. Zeming Fan
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  6. Xiaojun Yu
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Contributions

Z. Fan, X.Y. and M. Z. Aziz proposed the main idea and designed the main aspects of the project; W. Haider and M. Z. Aziz conceived and designed the experiments; M. A. Abbasi and H. F. Abbasi designed and implemented all the software required to performed the experiments, and performed the experiments; X. Yu and M. Z. Aziz evaluated the data; Z. Fan and X. Yu finalized the paper writing, review, and editing.

Corresponding author

Correspondence to Xiaojun Yu.

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The authors declare no potential conflict of interests.

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Ethical approval was not sought for the present study because the datasets utilized in this study are publicly available.

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Abbasi, M.A., Abbasi, H.F., Aziz, M.Z. et al. A novel precisely designed compact convolutional EEG classifier for motor imagery classification. SIViP 18, 3243–3254 (2024). https://doi.org/10.1007/s11760-023-02986-1

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  • DOI: https://doi.org/10.1007/s11760-023-02986-1

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