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

Original Article

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.

Graphical abstract

Keywords

Brain-computer interface (BCI) Filter tuning Genetic algorithm (GA) Motor imagery (MI) Temporal filters 

Notes

Acknowledgements

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

Supplementary material

11517_2018_1821_MOESM1_ESM.pdf (375 kb)
ESM 1 (PDF 374 kb).

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Copyright information

© International Federation for Medical and Biological Engineering 2018

Authors and Affiliations

  1. 1.Department of Electronics, Instrumentation & Control Engineering, School of Electrical & Electronics EngineeringFiji National UniversitySamabulaFiji
  2. 2.School of Engineering and Physics, Faculty of Science, Technology & EnvironmentThe University of the South PacificSuvaFiji
  3. 3.Institute for Integrated and Intelligent Systems (IIIS)Griffith UniversityBrisbaneAustralia
  4. 4.Laboratory for Medical Science MathematicsRIKEN Center for Integrative Medical SciencesYokohamaJapan

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