Feature Analysis of Multi-Channel Time Series EEG Based on Incremental Model


The KIPS Transactions:PartB , Vol. 16, No. 1, pp. 63-70, Feb. 2009
10.3745/KIPSTB.2009.16.1.63,   PDF Download:

Abstract

BCI technology is to control communication systems or machines by brain signal among biological signals followed by signal processing. For the implementation of BCI systems, it is required that the characteristics of brain signal are learned and analyzed in real-time and the learned characteristics are applied. In this paper, we detect feature vector of EEG signal on left and right hand movements based on incremental approach and dimension reduction using the detected feature vector. In addition, we show that the reduced dimension can improve the classification performance by removing unnecessary features. The processed data including sufficient features of input data can reduce the time of processing and boost performance of classification by removing unwanted features. Our experiments using K-NN classifier show the proposed approach 5% outperforms the PCA based dimension reduction.


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Cite this article
[IEEE Style]
S. H. Kim, H. J. Yang, K. S. Ng, "Feature Analysis of Multi-Channel Time Series EEG Based on Incremental Model," The KIPS Transactions:PartB , vol. 16, no. 1, pp. 63-70, 2009. DOI: 10.3745/KIPSTB.2009.16.1.63.

[ACM Style]
Sun Hee Kim, Hyung Jeong Yang, and Kam Swee Ng. 2009. Feature Analysis of Multi-Channel Time Series EEG Based on Incremental Model. The KIPS Transactions:PartB , 16, 1, (2009), 63-70. DOI: 10.3745/KIPSTB.2009.16.1.63.