Please note this excelent book for MEMS Biosensing!
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Authors
Chih-Wei Chang1, Li-Wei Ko1, Fu-Chang Lin1, Tung-Ping Su2, Tzyy-Ping Jung1, 3, Chin-Teng Lin1, Jin-Chern Chiou1, 4 1National Chiao Tung University, Hsinchu, Taiwan
2Taipei Veterans General Hospital, Taipei, Taiwan
3University of California, San Diego, CA, USA
4China Medical University, Taichung, Taiwan
Abstract
Electroencephalography (EEG) has been widely adopted to monitor changes in cognitive states, particularly stages of sleep, as EEG recordings contain a wealth of information reflecting changes in alertness and sleepiness. In this study, silicon dry electrodes based on Micro-Electro-Mechanical Systems (MEMS) were developed to bring high-quality EEG acquisition to operational workplaces. They have superior conductivity performance, large signal intensity, and are smaller in size than conventional (wet) electrodes. An EEG-based drowsiness estimation system consisting of a dry-electrode array, power spectrum estimation, principal component analysis (PCA)-based EEG signal analysis, and multivariate linear regression was developed to estimate drivers’ drowsiness levels in a virtual-reality-based dynamic driving simulator. The proposed system can help elders who are often affected by periods of tiredness and fatigue.
Journal Publisher Verlag Hans Huber ISSN 1662-9647 (Print) 1662-971X (Online) Collection Issue Category article Pages 107-113 DOI 10.1024/1662-9647/a000014
Authors
Chih-Wei Chang1, Li-Wei Ko1, Fu-Chang Lin1, Tung-Ping Su2, Tzyy-Ping Jung1, 3, Chin-Teng Lin1, Jin-Chern Chiou1, 41National Chiao Tung University,Hsinchu, Taiwan
2Taipei Veterans General Hospital,Taipei, Taiwan
3University of California,San Diego, CA, USA
4China Medical University,Taichung, Taiwan
Abstract
Electroencephalography (EEG) has been widely adopted to monitor changes in cognitive states, particularly stages of sleep, as EEG recordings contain a wealth of information reflecting changes in alertness and sleepiness. In this study, silicon dry electrodes based on Micro-Electro-Mechanical Systems (MEMS) were developed to bring high-quality EEG acquisition to operational workplaces. They have superior conductivity performance, large signal intensity, and are smaller in size than conventional (wet) electrodes. An EEG-based drowsiness estimation system consisting of a dry-electrode array, power spectrum estimation, principal component analysis (PCA)-based EEG signal analysis, and multivariate linear regression was developed to estimate drivers’ drowsiness levels in a virtual-reality-based dynamic driving simulator. The proposed system can help elders who are often affected by periods of tiredness and fatigue.
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