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

Enzeng Dong,Jigang Tong,Chao Chen,Yingjie Jiao,Shengzhi Du,Guangxu Zhu,Gualberto Asencio-Cortés

doi:10.1371/journal.pone.0198786

Abstract

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.

Highlights

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 [1,2,3,4,5]
For the two selected classes, Xi denotes an EEG sample of class i, Xi is a matrix of N × T, where N is the number of channels, T is the product of sampling frequency and acquisition, that is, the number of sampling points in a channel for one Motor imagery (MI) epoch
The four-class MI classification is transformed into six cases of two-class classification by one versus one common spatial pattern (OVO-Common spatial pattern (CSP))

Summary

Introduction

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 [1,2,3,4,5]. 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 [6,7,8,9]. A few EEG classification algorithms were proposed, for example, the linear discriminant analysis (LDA), the artificial neural networks (ANN), and the support vector machine (SVM), etc.

Methods

Results

Conclusion