tailieunhanh - Báo cáo hóa học: " Research Article Nonparametric Single-Trial EEG Feature Extraction and Classification of Driver’s Cognitive "

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Nonparametric Single-Trial EEG Feature Extraction and Classification of Driver’s Cognitive | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008 Article ID 849040 10 pages doi 2008 849040 Research Article Nonparametric Single-Trial EEG Feature Extraction and Classification of Driver s Cognitive Responses Chin-Teng Lin 1 Ken-Li Lin 1 2 Li-Wei Ko 1 Sheng-Fu Liang 3 Bor-Chen Kuo 4 and I-Fang Chung5 1 Department of Electrical and Control Engineering and Brain Research Center National Chiao-Tung University 1001 Ta Hsueh Road Hsinchu 300 Taiwan 2 Computer Center of Chung Hua University Hsinchu 707 Section 2 WuFu Road HsinChu 300 Taiwan 3 Department of Computer Science and Information Engineering National Cheng-Kung University No. 1 University Road Tainan 701 Taiwan 4 Graduate Institute of Educational Measurement and Statistics National Taichung University 140 Min-Shen Road Taichung 40306 Taiwan 5 Institute of Biomedical Informatics National Yang-Ming University No. 155 Section 2 Linong Street Taipei 112 Taiwan Correspondence should be addressed to I-Fang Chung ifchung@ Received 1 December 2007 Accepted 10 March 2008 Recommended by Chien-Cheng Lee We proposed an electroencephalographic EEG signal analysis approach to investigate the driver s cognitive response to trafficlight 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 andlinear 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 .