Comparison of Different Classication Methods for EEG-Based Brain Computer Interfaces: A Case Study
The performances of different off-line methods for two different Electroencephalograph (EEG) signal classification tasks – motor imagery and finger movement, are investigated in this paper. The classifiers based on linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), kernel fisher discriminant (KFD), support vector machine (SVM), multilayer perceptron (MLP), learning vector quantization (LVQ) neural network, k-nearest neighbor (k-NN), and decision tree (DT), are compared in terms of classification accuracy. The main purpose of this paper is to provide a fair and extensive comparison of some commonly employed classification methods under the same conditions so that the assessment of different classifiers will be more convictive. As a result, a guideline for choosing appropriate algorithms for EEG classification tasks is provided.
Boyu Wang Chi Man Wong Feng Wan Peng Un Mak Pui In Mak Mang I Vai
Department of Electrical and Electronics Engineering,Faculty of Science and Technology,University of Macau,Av. Padre Tomás Pereira,Taipa,Macau
国际会议
2009 IEEE International Conference on Information and Automation(2009年 IEEE信息与自动化国际学术会议)
珠海、澳门
英文
1416-1421
2009-06-22（万方平台首次上网日期，不代表论文的发表时间）