[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.5391/JKIIS.2013.23.2.178

Binary Classification Method using Invariant CSP for Hand Movements Analysis in EEG-based BCI System

Nguyen, Thanh Ha (School of Electrical and Electronics Engineering, Chung-Ang University)
Park, Seung-Min (School of Electrical and Electronics Engineering, Chung-Ang University)
Ko, Kwang-Eun (School of Electrical and Electronics Engineering, Chung-Ang University)
Sim, Kwee-Bo (School of Electrical and Electronics Engineering, Chung-Ang University)

Publication Information

Journal of the Korean Institute of Intelligent Systems / v.23, no.2, 2013 , pp. 178-183 More about this Journal

Abstract

In this study, we proposed a method for electroencephalogram (EEG) classification using invariant CSP at special channels for improving the accuracy of classification. Based on the naive EEG signals from left and right hand movement experiment, the noises of contaminated data set should be eliminate and the proposed method can deal with the de-noising of data set. The considering data set are collected from the special channels for right and left hand movements around the motor cortex area. The proposed method is based on the fit of the adjusted parameter to decline the affect of invariant parts in raw signals and can increase the classification accuracy. We have run the simulation for hundreds time for each parameter and get averaged value to get the last result for comparison. The experimental results show the accuracy is improved more than the original method, the highest result reach to 89.74%.

Keywords

Brain-Computer Interface (BCI); Electroencephalogram (EEG); Invariant Common Spatial Pattern; Independent Component Analysis; Auditory Stimuli;

Citations & Related Records

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