Research Article: Introducing chaos behavior to kernel relevance vector machine (RVM) for four-class EEG classification

Date Published: June 29, 2018

Publisher: Public Library of Science

Author(s): Enzeng Dong, Guangxu Zhu, Chao Chen, Jigang Tong, Yingjie Jiao, Shengzhi Du, Gualberto Asencio-Cortés.


This paper addresses a chaos kernel function for the relevance vector machine (RVM) in EEG signal classification, which is an important component of Brain-Computer Interface (BCI). The novel kernel function has evolved from a chaotic system, which is inspired by the fact that human brain signals depict some chaotic characteristics and behaviors. By introducing the chaotic dynamics to the kernel function, the RVM will be enabled for higher classification capacity. The proposed method is validated within the framework of one versus one common spatial pattern (OVO-CSP) classifier to classify motor imagination (MI) of four movements in a public accessible dataset. To illustrate the performance of the proposed kernel function, Gaussian and Polynomial kernel functions are considered for comparison. Experimental results show that the proposed kernel function achieved higher accuracy than Gaussian and Polynomial kernel functions, which shows that the chaotic behavior consideration is helpful in the EEG signal classification.

Partial Text

Brain-Computer Interface (BCI) is an interdisciplinary cutting-edge technology that establishes communication and control channels between human brain and an external computer or other intelligent electronic equipment [1–5]. Motor imagery (MI) based BCIs focus on converting the recorded electroencephalograph (EEG) during imagining limb or body movements, the so-called ‘idea’, into specific codes or commands to detect EEG signal behaviour or control the intelligent equipment [6–9].

In this paper, a new chaos kernel was proposed for relevance vector machine to classify four-class EEG of motor imagery. The raw EEG signals are addressed by 3-24 Hz band-pass filter to remove artifacts and uncorrelated frequency bands. And the four-class classification problem is transformed into six two-class problem under the framework of OVO-CSP method. Then the feature vectors extracted by OVO-CSP are sent to the RVM for classification.




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