Guest Editors
Guest Editor
Prof. Yuanpeng Zhang, Nantong University / The Hong Kong Polytechnic University, China Email: maxbirdzhang@ntu.edu.cn
Biography:
Yuanpeng Zhang is currently a full Professor with the department of medical informatics, Nantong University. He is also a Postdoctoral Fellow with the department of Health Information Technology, the Hong Kong Polytechnic University. He received his Ph.D. degree in Information Engineering School of Computer Application Technology of Jiangnan University in 2018. He has been a research assistant at the Department of computing, the Hong Kong Polytechnic University for one year. He has published about 40 papers in international/national journals including IEEE Transactions on Fuzzy Systems, IEEE Transactions on Systems, Man, and Cybernetics: Systems, IEEE Transactions on Intelligent Transportation Systems, Knowledge-based Systems, and ACM Transactions on Internet of Technology and IEEE/ACM Transactions on Computational Biology and Bioinformatics. His current research interests include deep learning, transfer learning, multi-view learning, multi-task learning, computational intelligence and their applications in smart medicine and brain-computer interface. More information can be found at ORCID (https://orcid.org/0000-0003-1736-3425).
Co-guest editor 1
Name: Yizhang Jiang
Title: Associate Professor
Email: yzjiang@jiangnan.edu.cn
Bio:
Yizhang Jiang received the Ph.D. degree from the School of Digital Media, Jiangnan University, Wuxi, China, in 2016. He is currently an Associate Professor of Computer Science of Jiangnan University. He has been a research assistant at the Department of computing, the Hong Kong Polytechnic University for nearly two years. He is also an international collaborator at the Quantitative Imaging Lab of the Case Western Reserve University(https://case.edu/medicine/qil/people/collaborators). He has authored or co-authored more than 60 research papers in international/national journals, including IEEE Trans. Fuzzy Systems, IEEE Trans. Neural Networks and Learning Systems, IEEE Trans. Cybernetics, ACM Trans. Intelligent Systems and Technology, and IEEE Trans. Neural Systems & Rehabilitation Engineering. His current research interests include deep learning, transfer learning, multi-view learning, multi-task learning, computational intelligence and their applications in smart medicine and BMI/BCI. He is a guest editor for Journal of Ambient Intelligence and Humanized Computing. More information on Jiang’s work can be found at ORCID (https://orcid.org/0000-0002-4558-9803).
Co-guest editor 2
Name: Shiyong Wu
Title: Associated Professor
Email:shiyong.wu@m.scnu.edu.cn
Bio:
Shiyong Wu is a professor at South China Normal University and used to be a visiting scholar at Monash University in Australia. He is interested in higher education and educational psychology. Currently, he is mainly devoted to graduate employability, personality, self-efficacy, job satisfaction, and occupational health psychology, especially for vulnerable populations, using quantitative and qualitative research methods. He has published a wide range of high-quality works and built high visibility in the international academic society. More information can be found at ORCID (https://orcid.org/my-orcid?orcid=0000-0002-5886-6646).
Summary
Emotions are the attitudes and experiences of human beings after comparing objective things with their own needs. Emotions can reflect a person's current mental health state, and also have an important impact on people's cognition, communication, and decision-making. Emotional changes are usually produced under the stimulus of the external environment and are accompanied by changes in individual representations and psychological responses, so they can be measured and simulated by scientific methods. Affective computing is an interdisciplinary research field involving multiple disciplines such as computer science, psychology, and cognitive science, aiming to research and develop theories and methods that can identify, explain, process and simulate human emotions and system. Changes in human emotions are usually accompanied by changes in physiological signals. The advantage of physiological signals compared with facial expressions or speech signals is that physiological signals can better reflect the real emotional state, while facial expressions and speech signals are not delicate enough to represent emotions and are easy to camouflage. Therefore, physiological signals are important input signals for affective computing. Among them, electroencephalography (EEG) is a signal obtained by collecting, amplifying and recording the weak bioelectrical signals generated by the human brain at the scalp through an EEG cap.
In EEG-based emotion recognition tasks, the core processes include preprocessing, feature extraction, feature selection, and emotion modeling. Among them, the machine learning algorithm in emotion modeling is the core. Transfer learning, active learning, multi-view (multi-modal) learning, and deep learning are widely used in emotion modeling. For example, style transfer mapping is applied to EEG-based multi-source domain cross-user emotion recognition. Even though machine learning algorithms have made some progress in EEG-based emotion recognition tasks, there are still many problems, including how to collaboratively mine the complementary information contained in multi-view data? How to transfer available knowledge from similar domains when there are insufficient training samples? How to guarantee the interpretability of a machine learning model?
This Special Issue aims to collect high-quality original research and review articles that address the open technical problems and challenges concerning emerging advanced computational intelligence in mental health based on EEG Signals
Potential topics include but are not limited to the following:
1. Advanced computational intelligence technologies for EEG feature extraction and selection.
2. Advanced multi-view & transfer learning for mental health evaluation based on EEG signals.
3. Active learning & transfer learning for mental health evaluation based on EEG signals.
4. Explainable machine learning model for mental health evaluation.
5. New design of EEG signal collection devices for mental health evaluation.
6. Advanced optimization methods for machine learning.
Keywords
Mental Health Evaluation, Advanced Computational Intelligence, Brain-Computer Interface, Emotion analysis
Login
Register
Submit a Paper
Propose a Special lssue