Abstract
Brain-Computer Interface (BCI) enhances the capability of a human brain in communicating and interacting with the environment directly. BCI plays an important role in natural cognition, which is to study the brain and behavior at work. Human cognitive functions such as perception, attention, situational awareness, and decision-making are omnipresent in our daily life activities. Hence, how to effectively enhance the human cognitive functions has become an important issue. Recently, many investigators have developed novel machine-learning algorithms based on computational intelligence (CI) technologies to monitor, maintain, or track the human cognitive states and operating performance. In this talk, I will first introduce the current status of BCI and its three major obstacles: lack of wearable EEG devices, various noise contamination, and lack of suitable adaptive cognitive modeling. I will then introduce the fundamental physiological changes of the human cognitive functions at work and then explain how to utilize these main Bio-findings and CI techniques (Bio-CI) to develop monitoring and feedback systems. I will also introduce some new CI-based machine learning algorithms to pursue real-time artifact removal and adaptive BCI, which can account for user variability, circadian variability, and task variability. The real-life applications of BCI on various aspects will also be introduced, including wearable computing, driving, clinics, homecare, personnel training, and computer gaming. Some future research directions will also be explored and discussed.

Biography
Dr. Chin-Teng Lin received the B.S. degree from the National Chiao-Tung University (NCTU), Taiwan in 1986, and the Master and Ph.D. degree in electrical engineering from Purdue University, West Lafayette, Indiana, U.S.A. in 1989 and 1992, respectively. He is currently a Chair Professor of Electrical and Computer Engineering and Director of Brain Research Center, National Chiao-Tung University. He is also invited as the International Faculty of the University of California at San Diego (UCSD) from 2012 and Honorary Professorship of University of Nottingham from 2014.
Dr. Lin’s research focuses on machine-intelligent systems, including algorithm development and system design. He has published over 190 journal papers, and is the coauthor of Neural Fuzzy Systems (Prentice-Hall) and author of Neural Fuzzy Control Systems with Structure and Parameter Learning (World Scientific). Dr. Lin has been serving as Editor-in-Chief of IEEE Transactions on Fuzzy Systems since 2011, and has served on the Board of Governors of IEEE Circuits and Systems Society, IEEE Systems, Man, and Cybernetics Society, and IEEE Computational Intelligence Society. Dr. Lin is an IEEE Fellow.

Cheng-Kok Koh, chengkok@purdue.edu, office: 63683, is hosting Prof. Lin.