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The IEEE Brain AI for Neurotechnology Workshop speakers

Information on speakers at the IEEE Brain AI for Neurotechnology Workshop at the World Congress on Computational Intelligence 2024 event in Yokohama, Japan


To find out more about the IEEE Brain AI for Neurotechnology Workshop, see the main conference webpage.

Keynote speaker

Prof. Mitsuo Kowato

Advanced Telecommunications Research Institute International, Japan

AI-based precision psychiatry

In oncology, genomic testing effectively segments patients and guides personalized treatment selection. In a parallel advancement within psychiatry, we are pioneering neurofeedback therapies utilizing brain circuit biomarkers and machine learning to provide tailored and superior care. During my leadership in Japanese national projects SRPBs and BMB, we built the world’s largest multicenter, multi-disease fMRI database, harmonized both prospectively and retrospectively. We also innovated a machine learning algorithm that neutralized variations in brain scans across different sites and created the first prospective diagnostic biomarker for major depression. Additionally, by applying hierarchical supervised and unsupervised learning, we categorized major depression into subtypes and predicted each subtype’s response to SSRI treatments.

For functional connectivity neurofeedback, we targeted specific brain circuits for normalization using circuit biomarkers. A model predicting working memory capacity was developed to enhance cognitive functions in patients with chronic schizophrenia. We treated patients with medication-resistant major depression using a state biomarker for depression. Improvements were also seen in facial expression recognition and sustained attention tasks in individuals with autism spectrum disorders. Moreover, decoded neurofeedback proved to be an effective, less distressing alternative to traditional exposure therapy for specific phobias and pain in a double-blind randomized controlled trial, as it operates without the user's conscious awareness of the induced brain information. In this talk, I summarize ATR and SRPBS/BMB efforts towards precision psychiatry.


MITSUO KAWATO earned a B.S. in Physics from Tokyo University in 1976 and M.E. and Ph.D. degrees in Biophysical Engineering from Osaka University in 1981. He served as a faculty member and lecturer at Osaka University from 1981 to 1988. Since 1988, he has been with ATR, becoming an ATR Fellow in 2004 and the Director of ATR Brain Information Communication Research Laboratories in 2010. From 2013 to 2018, he held a joint appointment as Research Leader of BMI Technology at SRPBS under the Japanese MEXT and was appointed as an R&D Principal Investigator of Brain/MINDS Beyond 3-1, AMED in 2018. Over these years, he has significantly contributed to the development of a multisite, multi-disorder fMRI database, brain circuit biomarkers, and advanced neurofeedback techniques. In 2018, he was also appointed as a Special Advisor at the Center for Advanced Integrated Intelligence Research at RIKEN.

His career in computational neuroscience and neural network modelling spans over forty years, and he has published approximately 350 papers, reviews, and books. His research interests include decoded neurofeedback, rs-fcMRI-based biomarkers for mental disorders, advanced fMRI neurofeedback therapy, simulation studies of dendritic spines, the feedback-error-learning model and its applications to both industrial and humanoid robots, movement trajectory formation, bi-directional theory of cortical interactions, cerebellar internal models, and robot teaching by demonstration.

Invited speakers

CT Lin

University of Technology Sydney, Australia

Brain Computer Interface in Augmented Reality and Metaverse

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, studying the brain and behaviour at work. Human cognitive functions such as action planning, intention, preference, perception, attention, situational awareness, and decision-making are omnipresent in our daily activities. BCI has been considered 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 the real world and virtual world. The 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 a bachelor's degree from the National Chiao-Tung University (NCTU), Taiwan in 1986, and a master's and PhD 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 the 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 450 journal papers (H-Index 94 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.

Prof. Dongrui Wu

Huazhong University of Science and Technology, China

Privacy-Preserving Brain-Computer Interfaces

A brain-computer interface (BCI) enables direct communication between the brain and an external device. Electroencephalogram EEG is the preferred input signal in non-invasive BCIs, due to its convenience and low cost. However, EEG signals inherently carry rich personal information, necessitating privacy protection. This talk demonstrates that multiple types of private information (user identity, gender, and BCI experience) can be easily inferred from EEG data, imposing a serious privacy threat to BCIs. It also introduces several approaches to protect such private information, without impacting the primary BCI task performance.


Dongrui Wu (IEEE Fellow) is a Professor at the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China. His research interests include brain-computer interface, machine learning, computational intelligence, and affective computing. He has more than 200 publications (13000+ Google Scholar citations; h=61). 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, 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 the National Champion of the China Brain-Computer Interface Competition in two successive years (2021-2022). Prof. Wu is the Editor-in-Chief of IEEE Transactions on Fuzzy Systems.

TP Jung

University of California San Diego, US; National Chiao Tung University, Taiwan; National Tsing Hua University, Taiwan; National Cheng Kung University, Taiwan; Tianjin University, China

BCI in Action: Translating Research into Real-World Solutions

Over the past three decades, there have been remarkable strides in fundamental neuroscience research and the development of cutting-edge neurotechnology. One of the pioneering innovations in this field is the Brain-Computer Interface (BCI), which enables a direct interface between the user's brain and external devices. Despite the success of BCI technologies, their use has largely been confined to well-controlled laboratory settings. Our research team is dedicated to overcoming existing barriers and translating laboratory demonstrations into real-world BCI applications. This presentation will explore the potential uses of EEG and BCI technology in various contexts, including clinical applications, cognitive state monitoring, educational settings, learning enhancement, and gaming.


Tzyy-Ping (TP) Jung is the Co-Director of the Center for Advanced Neurological Engineering, the Associate Director of the Swartz Center for Computational Neuroscience, and an Adjunct Professor in the Department of Bioengineering at the University of California, San Diego. He also holds adjunct professorships in the College of Education at National Tsing Hua University and the Department of Electrical Engineering at National Chiao Tung University in Taiwan. Dr. Jung pioneered transformative techniques for applying blind source separation to decompose multichannel EEG/MEG/ERP and fMRI data.

In recognition of his contributions to blind source separation for biomedical applications, he was elevated to an IEEE Fellow in 2015. He is also a Fellow of the Asia-Pacific Artificial Intelligence Association (AAIA). Dr. Jung’s research emphasizes the integration of cognitive science, computer science and engineering, neuroscience, bioengineering, and electrical engineering. His interdisciplinary work has been highly regarded and widely cited by peers.

Gerwin Schalk, Ph.D.

Visiting Professor, Fudan University/Huashan Hospital, Shanghai

Translation of Neurotechnologies

Neurotechnologies combine engineering methods and brain physiology information to realize devices that interface the brain with the outside world. Since the early 2000s, an increasing number of inspiring and encouraging examples have been the subject of high-profile articles and made headlines in popular media. However, with few exceptions, they have yet to produce distinct and long-lasting clinical benefits compared to existing (pharmaceutical or other) solutions. In this talk, I provide a systematic, state-of-the-art assessment of the opportunities and shortcomings of neurotechnology's engineering and scientific components, discuss the requirements and barriers to translation, and provide a comprehensive guiding framework to facilitate the clinical and commercial translation of neurotechnologies.


Dr. Schalk obtained his M.S. in Electrical Engineering and Computer Science from Graz University of Technology in Austria, his M.S. in Information Technology from Rensselaer Polytechnic Institute (RPI) in Troy, New York, and his Ph.D. in Computer and Systems Engineering from RPI.

He is interested in integrating scientific, engineering, and clinical concepts to advance our understanding of the brain and to use this new understanding to develop novel neurotechnologies that improve people's lives.

He authored or co-authored >130 peer-reviewed publications, one book and 17 chapters, has >27000 total citations and an H factor of 67, has given more than 270 invited lectures world-wide, is ranked #7 in BCI world-wide and #23 in neuroscience in China. His work has been extensively showcased by the media including features on CNN, NBC, CBS, Science Channel, and articles in New York Times Magazine, Discover Magazine, Forbes, Technology Review, and Wired. He is also listed in Who's Who in the World and Who's Who in America, and received several awards for his work.

Alexandre Gramfort

Meta Reality Labs, Paris, France

Neuromotor Interfaces for human-computer Interaction at Meta

Since the advent of computing, humans have sought interfaces for computer input that are expressive, intuitive, and universal. While diverse modalities have been developed, including keyboards, mice, and touchscreens, each requires interaction with an intermediary device that poses constraints, especially in mobile scenarios. Gesture-based interaction systems using cameras or inertial sensors support more natural interaction schemes but constrain users with cumbersome head-mounted camera systems or a confined field of view. Brain-computer interfaces (BCIs) have been imagined for decades to solve the interface problem by allowing for input to computing devices at the speed of thought. However high-bandwidth communication has only been demonstrated using invasive BCIs with interaction models designed for single individuals, an approach that cannot scale to the general public. In this talk, I will describe the development of a noninvasive neuromotor interface that allows for computer input using surface electromyography (sEMG). I will give examples whereby training machine learning models on thousands of participants, it is possible to develop generic sEMG neural network decoding models that work across many people without the need for per-person calibration, hence offering the first high-bandwidth neuromotor interface that directly leverages biosignals with performant out-of-the-box generalization across people.


Alexandre Gramfort is a Senior Research Scientist at Meta Reality Labs Paris, specializing in statistical machine learning and its applications in neuroscience and biosignal processing. Before this role, he led the MIND Team (previously known as Parietal) as a Senior Researcher at Inria and served as an Assistant Professor at Telecom Paris in signal processing and machine learning. Earlier in his career, Alexandre worked at the Martinos Center for Biomedical Imaging in Boston. Alexandre Gramfort made several major open-source software contributions with his work on the scikit-learn library for machine learning and by starting the mne-python project in 2010.

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