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An Approach of One-vs-Rest Filter Bank Common Spatial Pattern and Spiking Neural Networks for Multiple Motor Imagery Decoding

Wang, Hongtao and Tang, Cong and Xu, Tao and Li, Ting and Xu, Linfeng and Yue, Hongwei and Chen, Peng and Li, Junhua and Bezerianos, Anastasios (2020) 'An Approach of One-vs-Rest Filter Bank Common Spatial Pattern and Spiking Neural Networks for Multiple Motor Imagery Decoding.' IEEE Access, 8. 86850 - 86861. ISSN 2169-3536

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Abstract

Motor imagery (MI) is a typical BCI paradigm and has been widely applied into many aspects (e.g. brain-driven wheelchair and motor function rehabilitation training). Although significant achievements have been achieved, multiple motor imagery decoding is still unsatisfactory. To deal with this challenging issue, firstly, a segment of electroencephalogram was extracted and preprocessed. Secondly, we applied a filter bank common spatial pattern (FBCSP) with one-vs-rest (OVR) strategy to extract the spatio-temporal-frequency features of multiple MI. Thirdly, the F-score was employed to optimise and select these features. Finally, the optimized features were fed to the spiking neural networks (SNN) for classification. Evaluation was conducted on two public multiple MI datasets (Dataset IIIa of the BCI competition III and Dataset IIa of the BCI competition IV). Experimental results showed that the average accuracy of the proposed framework reached up to 90.09% (kappa: 0.868) and 81.33% (kappa: 0.751) on the two public datasets, respectively. The achieved performance (accuracy and kappa) was comparable to the best one of the compared methods. This study demonstrated that the proposed method can be used as an alternative approach for multiple MI decoding and it provided a potential solution for online multiple MI detection.

Item Type: Article
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 20 May 2020 08:26
Last Modified: 20 May 2020 09:15
URI: http://repository.essex.ac.uk/id/eprint/27575

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