Identification of Anisomerous Motor Imagery EEG Signals Based on Complex Algorithms.

Rensong Liu,Xin Zhou,Zhiwen Zhang,Zixuan Meng,Feng Duan

doi:10.1155/2017/2727856

Rensong Liu, Xin Zhou + Show 3 more

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https://doi.org/10.1155/2017/2727856

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Abstract

Motor imagery (MI) electroencephalograph (EEG) signals are widely applied in brain-computer interface (BCI). However, classified MI states are limited, and their classification accuracy rates are low because of the characteristics of nonlinearity and nonstationarity. This study proposes a novel MI pattern recognition system that is based on complex algorithms for classifying MI EEG signals. In electrooculogram (EOG) artifact preprocessing, band-pass filtering is performed to obtain the frequency band of MI-related signals, and then, canonical correlation analysis (CCA) combined with wavelet threshold denoising (WTD) is used for EOG artifact preprocessing. We propose a regularized common spatial pattern (R-CSP) algorithm for EEG feature extraction by incorporating the principle of generic learning. A new classifier combining the K-nearest neighbor (KNN) and support vector machine (SVM) approaches is used to classify four anisomerous states, namely, imaginary movements with the left hand, right foot, and right shoulder and the resting state. The highest classification accuracy rate is 92.5%, and the average classification accuracy rate is 87%. The proposed complex algorithm identification method can significantly improve the identification rate of the minority samples and the overall classification performance.

Highlights

Brain-computer interface (BCI) provides an efficient communication bridge between the human brain and external manageable devices [1]
In contrast to state visual-evoked potential (SSVEP) and P300, motor imagery (MI) is a self-induced brain activity, which is initiated by imaging certain limbs or other body parts to move without the help of outside inducing factors [5]
The following three steps are involved in the MI pattern recognition system: (1) preprocessing of raw EEG signals; (2) extraction of the features of each state of the EEG signal; and (3) building a pattern recognition classifier

Summary

Introduction

Brain-computer interface (BCI) provides an efficient communication bridge between the human brain and external manageable devices [1]. Among the signal-controlling BCI sources, the P300 [2], steady-state visual-evoked potential (SSVEP) [3], and motor imagery (MI) [4] signals are the most commonly used. An MI BCI system was first used based on this feature to assist humans with severe disabilities [6]. This system is used for humanoid controls [7], entertainment game designs [8], and aircraft flight controls [9]. The performance of this system is largely dependent on the number of MI motion commands that can be precisely classified

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