简介: |
Brain-machine interface (BMI) is to enhance human brain's capability in interacting with the environment through a direct pathway. For daily-life applications, BMI is extended to Brain-machine-body interface (BMBI) for close-loop interactions between body and external devices. BMBI plays an important role in natural cognition, which is to study the brain and behavior at work. Human cognitive functions such as perception, attention, memory and decision making are omnipresent in our daily life activities. For instance, driving is one of the most common attention-demanding tasks in our daily routine. When drivers lost their attention, they had appreciably reduced (or diminished) the perception, recognition and vehicle control abilities. Hence, how to effectively prevent and enhance the human cognitive functions has become a very important issue. Recently, many investigators had developed novel algorithms based on computational intelligence (CI) technologies to monitor, maintain, or track the human operating performance. In this lecture, we shall introduce the fundamental physiological changes of the human cognitive functions at work first and then explain how to utilize these main findings to develop the monitoring and feedback systems in the following two topics: (1) Wearable and wireless EEG devices for drivers based on innovative dry sensors; and (2) Cognitive state monitoring and prediction in driving. These research advancements can provide us new insights into the understanding of complex cognitive functions and lead to novel application enhancing our productivity and performance in face of real-world complications.
Dr. Chin-Teng Lin received the B.S. degree from National Chiao-Tung University (NCTU), Taiwan in 1986, and the Master and Ph.D. degree in electrical engineering from Purdue University, USA in 1989 and 1992, respectively. He is currently the Provost, Chair Professor of Electrical and Computer Engineering, and Director of Brain Research Center, National Chiao Tung University. Dr. Lin was elevated to be an IEEE Fellow for his contributions to biologically inspired information systems in 2005. He served on the Board of Governors at IEEE Circuits and Systems (CAS) Society in 2005-2008, IEEE Systems, Man, Cybernetics (SMC) Society in 2003-2005, IEEE Computational Intelligence Society in 2008-2010, Chair of IEEE Taipei Section in 2009-2010. Dr. Lin was the Distinguished Lecturer of IEEE CAS Society from 2003 to 2005. He served as the Deputy Editor-in-Chief of IEEE Transactions on Circuits and Systems-II in 2006-2008 and serve as the Editor-in-Chief of IEEE Transactions on Fuzzy Systems in 2011-2016. Dr. Lin was the General Chair of FUZZ-IEEE2011, Program Chair of IEEE International Conference on Systems, Man, and Cybernetics in 2005. Dr. Lin is the coauthor of Neural Fuzzy Systems (Prentice-Hall), and the author of Neural Fuzzy Control Systems with Structure and Parameter Learning (World Scientific). He has published more than 167 journal papers in the areas of neural networks, fuzzy systems, multimedia hardware/software, and cognitive neuro-engineering, including approximately 83 IEEE journal papers. Dr. Lin is a member of Tau Beta Pi, Eta Kappa Nu, and Phi Kappa Phi honorary societies. He has been the member of Board of Government (BoG) of Asia Pacific Neural Network Assembly (APNNA) since 2000; and the Council member of International Fuzzy System Association (IFSA) since 2000. Dr. Lin was the President of APNNA for 2004-2005. He has won the Outstanding Research Award granted by National Science Council (NSC), Taiwan, since 1997 to present, the Outstanding Electrical Engineering Professor Award granted by the Chinese Institute of Electrical Engineering (CIEE) in 1997, the Outstanding Engineering Professor Award granted by the Chinese Institute of Engineering (CIE) in 2000, and the 2002 Taiwan Outstanding Information-Technology Expert Award. Dr. Lin was also elected to be one of the 38th Ten Outstanding Rising Stars in Taiwan (2000). The main axis in Dr. Lin’s research career is to pursue for the biological-inspired intelligent systems, including algorithm development and system design. The research interests cover the basic circuitry level, to signal and information level, and to the system level, either from the biological or engineering point of views. The short-term target system is brain machine interface and the long-term goal is on brain-like intelligent system. |
