- Abstract:
- Biography
- Abstract:
- Biography
- Abstract:
- Biography
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 action planning, intention, preference, perception, attention, situational awareness, and decision-making are omnipresent in our daily life activities. BCI has been considered as the disruptive technology for the next-generation human computer interface in wearable computers and devices. In addition, there are many potential real-life impacts of BCI technology in both daily life applications for augmenting human performance, and daily care applications for elder/patients healthcare in real world and virtual world. Talk focus will be the applications of BCI technology on AR-based brain robot interface, BCI-based assistive glasses for the blind, Biofeedback for chronic pain mitigation, and BCI-based human-machine cooperation. The potential applications of BCI in the coming Metaverse will be also introduced in this talk.
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 Distinguished Professor, Director of UTS Human-centric AI Center, Co-Director of Australian AI Institute, and Director of CIBCI Lab, FEIT, UTS. He is also invited as the International Faculty of the University of California at San Diego (UCSD) from 2012 to 2020 and Honorary Professorship of University of Nottingham from 2014 to 2021.
Prof. Lin’s research focuses on machine-intelligent systems and brain computer interface, including algorithm development and system design. He has published over 460 journal papers (H-Index 98 based on Google Scholar) and is the co-author of Neural Fuzzy Systems (Prentice-Hall) and author of Neural Fuzzy Control Systems with Structure and Parameter Learning (World Scientific). Dr. Lin served as Editor-in-Chief of IEEE Transactions on Fuzzy Systems from 2011 to 2016 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. He is the Chair of the 2022-2023 CIS Awards Committee. Dr. Lin is an IEEE Fellow and received the IEEE Fuzzy Pioneer Award in 2017. He received the UTS Chancellor’s Medal of Research Excellence in 2015.
A brain-computer interface (BCI) enables direct communication between the brain and external devices. It can be used to research, enhance or repair human cognitive and sensory-motor functions. Electroencephalograms (EEGs) used in BCIs are weak, easily contaminated by interference and noise, non-stationary for the same subject, and varying across different subjects and sessions. Thus, sophisticated machine learning approaches are needed for accurate and reliable EEG decoding. This talk introduces how fuzzy logic can be used to conveniently extend classification algorithms in EEG-based BCIs to regression..
Dongrui Wu (IEEE Fellow) received a B.E in Automatic Control from the University of Science and Technology of China, Hefei, China, in 2003, an M.Eng in Electrical and Computer Engineering from the National University of Singapore in 2006, and a PhD in Electrical Engineering from the University of Southern California, Los Angeles, CA, in 2009. He is now Professor and Deputy Director of the Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China.
Prof. Wu's research interests include brain-computer interface, machine learning, computational intelligence, and affective computing. He has more than 200 publications (15000+ Google Scholar citations; h=65). He received the IEEE Computational Intelligence Society Outstanding PhD Dissertation Award in 2012, the IEEE Transactions on Fuzzy Systems Outstanding Paper Award in 2014, the IEEE Systems, Man and Cybernetics Society Early Career Award in 2017, the USERN Prize in Formal Sciences in 2020, the IEEE Transactions on Neural Systems and Rehabilitation Engineering Best Paper Award in 2021, the Chinese Association of Automation (CAA) Early Career Award in 2021, the Ministry of Education Young Scientist Award in 2022, and First Prize of the CAA Natural Science Award in 2023. His team won National Champion of the China Brain-Computer Interface Competition in four successive years (2021-2024). Prof. Wu is the Editor-in-Chief of IEEE Transactions on Fuzzy Systems.
In this era of Artificial Intelligence, a very common belief is that deep neural networks and in particular large language models (LLMs) are “all-cure” solutions. It is true that such systems have demonstrated their utility on a wide variety of applications. But they can also miserably fail with very simple reasoning problems. Moreover, all problems are not LLMs, all problems are not big-data problems, and more importantly such systems have issues that prevent them from using in critical applications like healthcare and defence. Among various criticisms of such systems, the more frequently talked ones are lack of interpretability, explainability, and trustworthiness (even in a very narrow sense of the word). Fuzzy rule-based systems as well as neuro-fuzzy systems have some inherent characteristics that can help to realize interpretability of the model, explainability, and trustworthiness. However, two major issues arise as the dimension of the data set increases and these issues severely affect the desirable attributes of fuzzy systems. In fact, designing of fuzzy rule-based systems or Takagi-Sugeno-Kang (TSK)-type neuro-fuzzy systems for high dimensional data becomes difficult due to numerical underflow and the “fake-minimum” problem. In this talk, first we shall present how feature-selection and fuzzy rule extraction can be done for high dimensional data in an integrated manner using the TSK-Neuro-Fuzzy framework avoiding the underflow and fake- minimum problem. Next, we shall discuss how high dimensional fuzzy systems can be designed without reducing the dimension of the data before designing the rule-based system. The proposed system guarantees avoidance of underflow. Not only that, we shall also discuss, how explainability and trustworthiness of the high-dimensional system can be recovered. In this context, we shall demonstrate the effectiveness of the proposed scheme using datasets of dimension more than 100,000 both for classification and regression problems – to the best of our knowledge it is the first attempt to use data of dimension more than 100000 to design fuzzy rule-based systems! .
Prof. Nikhil R. Pal is an INSA Senior Scientist at the Indian Statistical Institute, Calcutta, India and an Honorary Visiting Professor at the South Asian University, New Delhi. He was a Professor in the Electronics and Communication Sciences Unit and was the founding Head of the Center for Artificial Intelligence and Machine Learning of Indian Statistical Institute. His current research interest includes brain science, computational intelligence, machine learning and data mining. He was the Editor-in-Chief of the IEEE Transactions on Fuzzy Systems for the period January 2005-December 2010. He served/has been serving on the editorial /advisory board/ steering committee of several journals including the International Journal of Approximate Reasoning, Applied Soft Computing, International Journal of Neural Systems, Fuzzy Sets and Systems, IEEE Transactions on Fuzzy Systems and the IEEE Transactions on Cybernetics. He is a recipient of the 2015 IEEE Computational Intelligence Society (CIS) Fuzzy Systems Pioneer Award and 2021 IEEE CIS Meritorious Service Award. He has given many plenary/keynote speeches in different premier international conferences in the area of computational intelligence. He has served as the General Chair, Program Chair, and co-Program chair of several conferences. He has been a Distinguished Lecturer of the IEEE CIS (2010-2012, 2016-2018, 2022-2024) and was a member of the Administrative Committee of the IEEE CIS (2010-2012). He has served as the Vice-President for Publications of the IEEE CIS (2013-2016) and the President of the IEEE CIS (2018-2019). He is a Fellow of the West Bengal Academy of Science and Technology; Institution of Electronics and Tele Communication Engineers; National Academy of Sciences, India; Indian National Academy of Engineering; Indian National Science Academy; International Fuzzy Systems Association (IFSA); The World Academy of Sciences; and a Fellow of the IEEE, USA.