tailieunhanh - Báo cáo hóa học: "Research Article EEG-Based Subject- and Session-independent Drowsiness Detection: An Unsupervised Approach"

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 EEG-Based Subject- and Session-independent Drowsiness Detection: An Unsupervised Approach | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008 Article ID 519480 11 pages doi 2008 519480 Research Article EEG-Based Subject- and Session-independent Drowsiness Detection An Unsupervised Approach Nikhil R. Pal 1 2 3 Chien-Yao Chuang 1 2 Li-Wei Ko 1 2 Chih-Feng Chao 1 2 Tzyy-Ping Jung 1 2 4 Sheng-Fu Liang 5 and Chin-Teng Lin1 2 1 Department of Computer Science National Chiao-Tung University 1001 University Road Hsinchu 30010 Taiwan 2 Brain Research Center National Chiao-Tung University 1001 University Road Hsinchu 30010 Taiwan 3 Computer and Communication Sciences Division Electronics and Communication Sciences Unit Indian Statistical Institute 203 Barrackpore Trunk Road Kolkata 700108 India 4 Institute for Neural Computation University of California of San Diego 4150 Regents Park Row La Jolla CA 92037 USA 5 Department of Computer Science and Information Engineering National Cheng-Kung University University Road Tainan 701 Taiwan Correspondence should be addressed to Chin-Teng Lin ctlin@ Received 2 December 2007 Revised 25 June 2008 Accepted 22 July 2008 Recommended by Chien-Cheng Lee Monitoring and prediction of changes in the human cognitive states such as alertness and drowsiness using physiological signals are very important for driver s safety. Typically physiological studies on real-time detection of drowsiness usually use the same model for all subjects. However the relatively large individual variability in EEG dynamics relating to loss of alertness implies that for many subjects group statistics may not be useful to accurately predict changes in cognitive states. Researchers have attempted to build subject-dependent models based on his her pilot data to account for individual variability. Such approaches cannot account for the cross-session variability in EEG dynamics which may cause problems due to various reasons including electrode displacements environmental noises and skin-electrode .