Please use this identifier to cite or link to this item:
https://doi.org/10.1109/ACCESS.2020.2992631
Title: | An Approach of One-vs-Rest Filter Bank Common Spatial Pattern and Spiking Neural Networks for Multiple Motor Imagery Decoding | Authors: | Wang, H. Tang, C. Xu, T. Li, T. Xu, L. Yue, H. Chen, P. Li, J. Bezerianos, A. |
Keywords: | Electroencephalogram filter bank common spatial pattern (FBCSP) motor imagery (MI) spiking neural networks (SNN) |
Issue Date: | 2020 | Publisher: | Institute of Electrical and Electronics Engineers Inc. | Citation: | Wang, H., Tang, C., Xu, T., Li, T., Xu, L., Yue, H., Chen, P., Li, J., Bezerianos, A. (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. ScholarBank@NUS Repository. https://doi.org/10.1109/ACCESS.2020.2992631 | 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. © 2013 IEEE. | Source Title: | IEEE Access | URI: | https://scholarbank.nus.edu.sg/handle/10635/196242 | ISSN: | 2169-3536 | DOI: | 10.1109/ACCESS.2020.2992631 |
Appears in Collections: | Elements Staff Publications |
Show full item record
Files in This Item:
File | Description | Size | Format | Access Settings | Version | |
---|---|---|---|---|---|---|
10_1109_ACCESS_2020_2992631.pdf | 3.64 MB | Adobe PDF | OPEN | None | View/Download |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.