In order to improve the accuracy of classification with small amount of motor imagery training data on the development of brain-computer interface (BCI) systems, we proposed an analyzing method to automatically select the characteristic parameters based on correlation coefficient analysis. Throughout the five sample data of dataset Ⅳa from 2005 BCI Competition, we utilized short-time Fourier transform (STFT) and correlation coefficient calculation to reduce the number of primitive electroencephalogram dimension, then introduced feature extraction based on common spatial pattern (CSP) and classified by linear discriminant analysis (LDA). Simulation results showed that the average rate of classification accuracy could be improved by using correlation coefficient feature selection method than those without using this algorithm. Comparing with support vector machine (SVM) optimization features algorithm, the correlation coefficient analysis can lead better selection parameters to improve the accuracy of classification.
Citation：
ZHOUJinzhi, TANGXiaofang. Electroencephalogram Feature Selection Based on Correlation Coefficient Analysis. Journal of Biomedical Engineering, 2015, 32(4): 735-739. doi: 10.7507/1001-5515.20150134
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Copyright © the editorial department of Journal of Biomedical Engineering of West China Medical Publisher. All rights reserved
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- 1. WOLPAW J R,BIRBAUMER N,HEETDERKS W J,et al.Brain-computer interface technology:a review of the first international meeting[J].IEEE Trans Rehabil Eng,2000,8(2):164-173.
- 2. MAKEIG S,KOTHE C,MULLEN T,et al.Evolving signal processing for brain-computer interfaces[J].Proceedings of the IEEE,2012,100(Special Centennial Issue):1567-1584.
- 3. 裴晓梅,郑崇勋.基于Fisher判据时频分析的运动相关脑电特征选择及优化[J].西安交通大学学报,2008,42(8):1026-1030.
- 4. 张胜,王蔚.基于支持向量机的BCI导联选择算法[J].中国生物医学工程学报,2009,28(4):624-627.
- 5. 吴婷.基于基因优化的脑电信号特征选择[J].仪器仪表学报,2011,32(12):2706-2711.
- 6. LU Haiping,ENG H L,GUAN Cuntai,et al.Regularized common spatial pattern with aggregation for EEG classification in small-sample setting[J].IEEE Trans Biomed Eng,2010,57(12):2936-2946.
- 7. WU Shanglin,WU Chunwei,PAL N R,et al.Common spatial pattern and linear discriminant analysis for motor imagery classification[C]//2013 IEEE Symposium on Computational Intelligence,Cognitive Algorithms,Mind,and Brain (CCMB).Singapore:2013:146-151.
- 8. 王毅军.基于节律调制的脑-机接口系统-从离线到在线的跨越[D].北京:清华大学,2007.
- 9. 陈悦.关于"脑-计算机"中脑电信号分类的研究[D].南京:南京邮电大学,2013.
- 10. MAHANTA M S,AGHAEI A S,PLATANIOTIS K N.Regularized LDA based on separable scatter matrices for classification of spatio-spectral EEG patterns[C]//2013 IEEE International Conference on Acoustics,Speech and Signal Processing (ICASSP).Vancouver,BC:2013:1237-1241.
