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Diverse Feature Blend Based on Filter-Bank Common Spatial Pattern and Brain Functional Connectivity for Multiple Motor Imagery Detection

Wang, Hongtao and Xu, Tao and Tang, Cong and Yue, Hongwei and Chen, Chuangquan and Xu, Linfeng and Pei, Zian and Dong, Jiajun and Bezerianos, Anastasios and Li, Junhua (2020) 'Diverse Feature Blend Based on Filter-Bank Common Spatial Pattern and Brain Functional Connectivity for Multiple Motor Imagery Detection.' IEEE Access, 8. 155590 - 155601. ISSN 2169-3536

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Abstract

Motor imagery (MI) based brain-computer interface (BCI) is a research hotspot and has attracted lots of attention. Within this research topic, multiple MI classification is a challenge due to the difficulties caused by time-varying spatial features across different individuals. To deal with this challenge, we tried to fuse brain functional connectivity (BFC) and one-versus-the-rest filter-bank common spatial pattern (OVR-FBCSP) to improve the robustness of classification. The BFC features were extracted by phase locking value (PLV), representing the brain inter-regional interactions relevant to the MI, whilst the OVR-FBCSP is used to extract the spatial-frequency features related to the MI. These diverse features were then fed into a multi-kernel relevance vector machine (MK-RVM). The dataset with three motor imagery tasks (left hand MI, right hand MI, and feet MI) was used to assess the proposed method. Experimental results not only showed that the cascade structure of diverse feature fusion and MK-RVM achieved satisfactory classification performance (average accuracy: 83.81%, average kappa: 0.76), but also demonstrated that BFC plays a supplementary role in the MI classification. Moreover, the proposed method has a potential to be integrated into multiple MI online detection owing to the advantage of strong time-efficiency of RVM.

Item Type: Article
Uncontrolled Keywords: Multiple motor imagery, filter-bank common spatial pattern (FBCSP), phase locking value (PLV), brain functional connectivity (BFC), multi-kernel relevance vector machine (MK-RVM)
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 23 Sep 2020 10:38
Last Modified: 23 Sep 2020 10:38
URI: http://repository.essex.ac.uk/id/eprint/28628

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