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A new parameter tuning approach for enhanced motor imagery EEG signal classification

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Abstract

A brain-computer interface (BCI) system allows direct communication between the brain and the external world. Common spatial pattern (CSP) has been used effectively for feature extraction of data used in BCI systems. However, many studies show that the performance of a BCI system using CSP largely depends on the filter parameters. The filter parameters that yield most discriminating information vary from subject to subject and manually tuning of the filter parameters is a difficult and time-consuming exercise. In this paper, we propose a new automated filter tuning approach for motor imagery electroencephalography (EEG) signal classification, which automatically and flexibly finds the filter parameters for optimal performance. We have evaluated the performance of our proposed method on two public benchmark datasets. Compared to the existing conventional CSP approach, our method reduces the average classification error rate by 2.89% and 3.61% for BCI Competition III dataset IVa and BCI Competition IV dataset I, respectively. Moreover, our proposed approach also achieved lowest average classification error rate compared to state-of-the-art methods studied in this paper. Thus, our proposed method can be potentially used for developing improved BCI systems, which can assist people with disabilities to recover their environmental control. It can also be used for enhanced disease recognition such as epileptic seizure detection using EEG signals.

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Acknowledgements

Special thanks to the editors and anonymous reviewers for their positive and constructive comments and suggestions that helped improve our manuscript.

Funding

This work was supported by the Faculty Research Committee of the University of the South Pacific, Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan, and College Research Committee of Fiji National University.

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Authors and Affiliations

  1. Department of Electronics, Instrumentation & Control Engineering, School of Electrical & Electronics Engineering, Fiji National University, Samabula, Fiji

    Shiu Kumar

  2. School of Engineering and Physics, Faculty of Science, Technology & Environment, The University of the South Pacific, Suva, Fiji

    Shiu Kumar & Alok Sharma

  3. Institute for Integrated and Intelligent Systems (IIIS), Griffith University, Brisbane, Australia

    Alok Sharma

  4. Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan

    Alok Sharma

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  1. Shiu Kumar
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Correspondence to Alok Sharma.

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Kumar, S., Sharma, A. A new parameter tuning approach for enhanced motor imagery EEG signal classification. Med Biol Eng Comput 56, 1861–1874 (2018). https://doi.org/10.1007/s11517-018-1821-4

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