標題: Nonparametric single-trial EEG feature extraction and classification of driver's cognitive responses
作者: Lin, Chin-Teng
Lin, Ken-Li
Ko, Li-Wei
Liang, Sheng-Fu
Kuo, Bor-Chen
Chung, I-Fang
電控工程研究所
腦科學研究中心
Institute of Electrical and Control Engineering
Brain Research Center
公開日期: 2008
摘要: We proposed an electroencephalographic (EEG) signal analysis approach to investigate the driver's cognitive response to traffic-light experiments in a virtual-reality-(VR-) based simulated driving environment. EEG signals are digitally sampled and then transformed by three different feature extraction methods including nonparametric weighted feature extraction (NWFE), principal component analysis (PCA), and linear discriminant analysis (LDA), which were also used to reduce the feature dimension and project the measured EEG signals to a feature space spanned by their eigenvectors. After that, the mapped data could be classified with fewer features and their classification results were compared by utilizing two different classifiers including k nearest neighbor classification (KNNC) and naive bayes classifier (NBC). Experimental data were collected from 6 subjects and the results show that NWFE+NBC gives the best classification accuracy ranging from 71%similar to 77%, which is over 10%similar to 24% higher than LDA+KNN1. It also demonstrates the feasibility of detecting and analyzing single-trial EEG signals that represent operators' cognitive states and responses to task events. Copyright (C) 2008 Chin-Teng Lin et al.
URI: http://hdl.handle.net/11536/9946
http://dx.doi.org/10.1155/2008/849040
ISSN: 1687-6172
DOI: 10.1155/2008/849040
期刊: EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING
Appears in Collections:Articles


Files in This Item:

  1. 000256730600001.pdf