Guest Editors
Lim Lam Ghai
Monash University, School of Engineering, Department of Electrical and Robotics Engineering
Lim Lam Ghai received Bachelor's, Master's, and PhD degrees in Electrical and Electronic Engineering from Universiti Teknologi PETRONAS, Malaysia. He received the best Master's and PhD dissertation awards from IEEE Malaysia. He is currently a lecturer in the Department of Electrical and Robotics Engineering at Monash University Malaysia. His research focuses on neuroimaging, brain-computer interfaces (BCIs), and artificial intelligence (AI). He has more than six years of research experience in functional near-infrared spectroscopy, covering everything from experimental design to data acquisition, processing, analysis, and applications. He also explores the use of AI in neuroimaging. Ultimately, he aims to develop reliable and robust BCIs as alternative tools for diagnosing, monitoring, and treating neurological and psychiatric disorders.
Tong Boon Tang
Universiti Teknologi PETRONAS, Institute of Health & Analytics
Tong Boon Tang received the B.Eng. (Hons.) and PhD degrees from The University of Edinburgh. He is a Professor and the Institute Director of Health and Analytics with Universiti Teknologi PETRONAS. His research interest includes biomedical instrumentation, from device and measurement to data fusion. He received the Lab on Chip Award in 2006, the IET Nanobiotechnology Premium Award, in 2008, the IET Mountbatten Medal, in 2020, and the Top Research Scientists Malaysia, in 2021. He served as the Higher Centre of Excellence (HICoE) Council Secretary and the Chair for the IEEE Circuits and Systems Society Malaysia Chapter. He is also a Fellow of IET and a Chartered Engineer.
Collection Overview
Brain dysfunction is one of the biggest threats to humanity and can seriously affect one’s quality of life. Neuroscience advancements have improved understanding of the neural mechanisms underlying neurological and psychiatric disorders. Several non-invasive functional neuroimaging modalities, such as electroencephalography, functional magnetic resonance imaging, positron emission tomography, magnetoencephalography, and functional near-infrared spectroscopy, are used to acquire brain activity. Utilizing these modalities paves the way for developing brain-computer interfaces (BCIs) as alternatives to diagnosing, monitoring, and treating brain disorders.
However, developing robust and reliable BCIs is challenging for clinical use due to the high variability in neuroimaging data. In the hope of accelerating BCI-related work in the research community, we aim to contribute to the development of BCIs through this Method Collection.
The scope includes, but is not limited to:
- The design of BCIs for assessing brain activity
- The integration of modern algorithms, e.g., artificial intelligence with BCIs
- The development and deployment of BCIs in clinical settings
Articles
Motor Imagery Performance Through Embodied Digital Twins in a Virtual Reality-Enabled Brain-Computer Interface Environment
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Cited by 1
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2024
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Kishor Lakshminarayanan1, Rakshit Shah2, Vadivelan Ramu1, Deepa Madathil3, Yifei Yao4, Inga Wang5, Brahim Brahmi6, Mohammad Habibur Rahman7
1Department of Sensors and Biomedical Tech, School of Electronics Engineering, Vellore Institute of Technology, 2Department of Orthopaedic Surgery, University of Arizona, 3Jindal Institute of Behavioural Sciences, O. P. Jindal Global University, 4Soft Tissue Biomechanics Laboratory, Med-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, 5Department of Occupational Science & Technology, University of Wisconsin-Milwaukee, 6Electrical Engineering, Collège Ahuntsic, 7Department of Mechanical Engineering, BioRobotics Lab, University of Wisconsin-Milwaukee