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WaveCSP: a robust motor imagery classifier for consumer EEG devices

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Abstract

There is an increasing demand for reliable motor imagery (MI) classification algorithms for applications in consumer level brain-computer interfacing (BCI). For the practical use, such algorithms must be robust to both device limitations and subject variability, which make MI classification a challenging task. This study proposes methods to study the effect of limitations including a limited number of electrodes, limited spatial distribution of electrodes, lower signal quality, subject variabilities and BCI literacy, on the performance of MI classification. To mitigate these limitations, we propose a machine learning approach, WaveCSP that uses 24 features extracted from EEG signals using wavelet transform and common spatial pattern (CSP) filtering techniques. The algorithm shows better performance in terms of subject variability compared to existing work. The application of WaveCSP to Physionet MI database shows more than 50% of the 109 subjects achieving accuracy higher than 64%. The data obtained from a commercial EEG headset using the same experimental protocol result in up to four out of five subjects who had prior BCI experience (out of a total of 25 subjects) performing with accuracy higher than 64%.

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Acknowledgements

This work was supported by the Singapore Academic Research Fund under Grant R-397-000-227-112. (corresponding author: Hongliang Ren). The authors would like to acknowledge Cai Xin Chen, National University of Singapore for his assistance in organizing and conducting experiment trials.

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Authors and Affiliations

  1. Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore

    Mohamed Athif & Hongliang Ren

  2. Department of Electronic and Telecommunication Engineering, University of Moratuwa, Moratuwa, Sri Lanka

    Mohamed Athif

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  1. Mohamed Athif
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  2. Hongliang Ren
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Correspondence to Hongliang Ren.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the National University of Singapore and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.

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Informed consent was obtained from all individual participants included in the study. No identifiable personal information is disclosed.

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Athif, M., Ren, H. WaveCSP: a robust motor imagery classifier for consumer EEG devices. Australas Phys Eng Sci Med 42, 159–168 (2019). https://doi.org/10.1007/s13246-019-00721-0

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  • DOI: https://doi.org/10.1007/s13246-019-00721-0

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