UM
Comparison of Different Classification Methods for EEG-Based Brain Computer Interfaces: A Case Study
Boyu Wang; Chi Man Wong; Feng Wan; Peng Un Mak; Pui In Mak; Mang I Vai
2009-11-23
Conference NameIEEE International Conference on Information and Automation (ICIA 2009)
Source Publication2009 IEEE International Conference on Information and Automation, ICIA 2009
Pages1416-1421
Conference Date22-24 June 2009
Conference PlaceZhuhai, Macau, China
CountryChina
PublisherIEEE
Abstract

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. 

DOIhttps://doi.org/10.1109/ICINFA.2009.5205138
URLView the original
Indexed BySCI
Language英语
WOS Research AreaAutomation & Control Systems ; Computer Science ; Engineering
WOS SubjectAutomation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000277076800253
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Document TypeConference paper
CollectionUniversity of Macau
AffiliationDepartment of Electrical and Electronics Engineering, Faculty of Science and Technology, University of Macau, Av. Padre Tomás Pereira,Taipa, Macau.
First Author AffilicationFaculty of Science and Technology
Recommended Citation
GB/T 7714
Boyu Wang,Chi Man Wong,Feng Wan,et al. Comparison of Different Classification Methods for EEG-Based Brain Computer Interfaces: A Case Study[C]:IEEE,2009:1416-1421.
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