Centre for Brain-Computing Research (CBCR)

Objectives

The Centre for Brain-Computing Research (CBCR) focuses on fundamental and applied brain-computer interface (BCI) research.

Our research aims to understand the neural mechanisms of motor and cognitive processes and quantify them from the associated manifestations in brain signals. We are also developing robust, high-performance decoding algorithms using machine learning, especially deep learning, and signal processing approaches. Our objective is to advance the field of BCI and explore its potential applications in various domains.

 

Research Topics

  • Deep learning algorithms for motor activity decoding (non-invasive BCI) 
    • Hand/wrist/upper limb motor decoding
    • Continuous lower limb joint decoding
  • Understanding of cognitive mechanisms and quantifying brain states from brain signals 
    • Continuous attention, arousal, fatigue detection
    • Affective computing for multimodal continuous emotion classification
  • Silent speech decoding from multimodalities (EEG/MEG/fMRI)
  • Decoding olfactory responses from brain signals 
  • Explainable AI for brain decoding modelling

Application Areas

Research Focus

Research Areas 

  • Brain-Computer Interfaces (Algorithms, Devices, Systems, Applications)
  • Cognitive Processes and Rehabilitation
  • Neural Signal Processing
  • Medical Image Processing
  • Data Analytics
  • Artificial Intelligence
  • Machine Learning

 

 Current and past projects
  • Next-Generation Brain-Computer-Brain Platform – A Holistic Solution for the Restoration & Enhancement of Brain Functions (NOURISH)
  • Scent Digitalization and Computation
  • Voice Biomarker for the Detection of Subsyndrome Depression 
  • MATAI: An AI-Powered Generalist Material Discovery Platform
  • Artificial intelligence driven Brain-Computing Research
  • Machine Learning Based Approach for Sensory Stimulated EEG Signal Classification
  • Silent Communication Proof-of-Concept Using Advanced Non-Invasive Brain Computer Interface and Machine Learning Methods
  • Early Detection of Mild Cognitive Impairment (MCI) through a Novel Digital Assessment
  • Resource-Efficient AI “Deep Transfer Learning, Adaptive Algorithms, Neurorehabilitation”
  • Brain-Computer Interface-based Emotion Regulation for General Anxiety
  • Neural Network Reorganization Associated with Upper Limb Motor Recovery in Stroke Patients- Establishing A Prognostic Model and Tailoring Neuromodulation for Rehabilitation
  • Brain Computer Interface Driven Stroke Therapy - Future Health Technologies (FHT)
  • Deep Learning in Neuroimaging Analysis for Stroke 
  • Program on Advanced Brain-Computer Interface Technologies for Mental Healthcare 
  • Research and Development in the Frontiers of Brain-Computer-Brain Interactions
  • Effectiveness of a Brain-Computer Interface-based Programme for the Treatment of Autism Spectrum Disorders and Attention Deficit Hyperactivity Disorders in Children: A Pilot Study.
  • Development of Interactive System for Brain-Computer Interfaces
  • Brain-computer Interface in Cognition and Rehabilitation
  • Arousal Detection and Training for Social Anxiety Disorder (SAD) 
  • Robust Neural Decoding and Control System for a Brain Machine Interface
  • Intelligent Wearable Neural Sensing System (iWENS)
  • Scent Preference Identification based on EEG (SPIE)
  • Brain-computer Interface System for Training Memory and Attention in Healthy Elderly and Elderly with Mild Cognitive Impairment
  • Simultaneous Multiscale Hyperspectral Near-infrared (NIR) Optical Imaging and MRI for Functional and Molecular Imaging
  • EEG-Based Brain Computer Interface for Cognitive Enhancement in Elderly with Age-Related Cognitive Decline and Mild Cognitive impairment (3E-COG) 
  • NeuroDevice Phase I: Neural Signal Processing
  • Advanced Rehabilitation Therapy for Stroke based on Brain Computer Interface (ArtsBCI)
  • Intelligent System for Neural Critical Care (iSyncc)
  • Brain Computer Interface for Attention Deficit Hyperactivity Disorder (ADHD) Treatment
  • Effectiveness of a Brain-Computer Interface-based Treatment for Attention Deficit and Hyperactivity Disorder (ADHD)
  • BCI-Based Robotic Rehabilitation for Stroke 
  • Brainy Communicator
  • Non-invasive Brain Stimulation and Brain-Computer Interface Assisted Motor Imagery for Rehabilitation of Mobility After Stroke 
  • A Brain-Computer Interface Based Intervention versus Sham Intervention for the Treatment of ADHD – a Double-Blind Randomized Controlled Trial
  • Assistive Soft Robotic Glove Intervention using Brain-Computer Interface for Elderly Stroke Patients
  • Affect Regulation Based on Brain-computer Interface Towards Treatment for Depression
  • Feasibility Study of BCI-based Adaptive Sensing and Feedback Training for Chronic Pain Management
  • Functional Magnetic Resonance Imaging Investigation of the Effects of Brain-Computer Interfaced Based Training on Selective Attention and Response Inhibition in Children with Attention Deficit Hyperactivity Disorder (ADHD) 
  • Combined Transcranial Direct Current Stimulation and Motor Imagery-based Robotic Arm Training for Stroke Rehabilitation – a Feasibility Study 
  • Automated Video-EEG Analytic System in Seizure Detection in the Epilepsy Monitoring Unit 

Our Team

Prof Cuntai GUAN

Personal profile: https://dr.ntu.edu.sg/cris/rp/rp01023

Fellow of SAEng, NAI, IEEE, AIMBE

President’s Chair in Computer Science and Engineering
Professor, School of Computer Science and Engineering
Professor, Lee Kong Chian School of Medicine (Courtesy Appointment)

Deputy Dean, College of Computing and Data Science 
Director, Centre for Brain-Computing Research (CBCR)
Co-Director, Rehabilitation Research Institute of Singapore (RRIS)

College of Computing and Data Science (CCDS)

Nanyang Technological University, Singapore (NTU) www.ntu.edu.sg

Email: ctguan@ntu.edu.sg

Phone: (65) 6790 6205

Office: N4-02B-52

Current appointments and positions:

President’s Chair in Computer Science and Engineering, NTU

  • Professor, College of Computing and Data Science (CCDS), NTU
  • Professor, Lee Kong Chian School of Medicine (Courtesy Appointment)
  • Deputy Dean, College of Computing and Data Science 
  • Director, Centre for Brain-Computing Research (CBCR)
  • Co-Director, Rehabilitation Research Institute of Singapore (RRIS)
  • Steering Committee, Cognitive Neuroimaging Centre & Cognition and Behavioural Sciences, NTU
  • Fellow, SAEng (Academy of Engineering, Singapore)
  • Fellow, NAI (National Academy of Inventors, USA)
  • Fellow, IEEE
  • Fellow, AIMBE

 

Research interests:

Brain-Computer Interfaces (BCI)

  • Neural Signal Processing
  • Medical Image Processing
  • Machine Learning
  • Data Analytics
  • Artificial Intelligence

     

    Highlights of research outcomes:

  • Robust Brain-computer Interface algorithms for motor imagery (FBCSP, regularization, session-to-session adaptation, channel selection, deep learning, etc.)
  • Asynchronous and adaptive P300 BCI and its use in wheelchair control
  • fNIRS based Brain-computer interfaces
  • Brain-computer Interface for stroke rehabilitation with significant clinical outcome
  • Innovative system for the treatment of Attention Deficit and Hyperactivity Disorders (ADHD) in children with significant efficacy outcome
  • Effective cognitive training for elderly with cognitive decline
  • Novel use of BCI technologies in, for example, sleep, depression, anxiety
  • Published 400 peer-reviewed journal and conference papers
  • 26 granted patents and patent applications (licensed 17 patents/technologies to 6 Singapore and US companies)

    Awards and Honors:

  • Nanyang Research Award, 2023
  • Elected Fellow of NAI (National Academy of Inventors, USA), Class of 2022 (Citation: have demonstrated a highly prolific spirit of innovation in creating or facilitating outstanding inventions that have made a tangible impact on the quality of life, economic development, and welfare of society)
  • King Salman Award for Disability Research (Technical Branch), 2022
  • Elected Fellow of SAEng (Academy of Engineering, Singapore), 2022
  • President’s Chair in Computer Science and Engineering, NTU, 2021
  • Elected Fellow of AIMBE (American Institute for Medical and Biological Engineering), Class of 2021 (Citation: for outstanding contributions to brain-computer interfaces and neural engineering)
  • Elected Fellow of IEEE (Institute of Electrical and Electronics Engineers), Class of 2018 (Citation: for contributions to brain-computer interfaces and applications)
  • APSIPA Distinguished Lecturer, 2017-2018
  • Finalist of President Technology Award, Singapore, 2014 (for contributions to BCI technologies and medical applications)
  • Achiever of the Year (Research) Award, Institute for Infocomm Research, A*STAR, 2011
  • The Annual BCI Research Award (First Prize), USA, 2010 (for contributions to BCI for stroke rehabilitation).
  • IES Prestigious Engineering Achievement Award, 2009 (for contributions to the development of BCI technology and its applications).
  • Top winner of International BCI Competition IV, 3 EEG categories, 2008
  • Top winner of International BCI Competition III, Dataset IIIa, 2005
  • Samsung Digital Hope Award, 2004 (to recognize the development of Brainy Communicator)

     

    Recent keynotes and invited talks:

  • Keynote speech “Research on Brain-Computer Interface and Applications” at the First China Brain-Machine Intelligence Conference, Dec 20 - 21, 2024, Hangzhou, China. 
  • Keynote speech “Brain-Computer Interface-based Intervention for General Anxiety” at the 2nd Brain-Computer Interface Conference, Nov 16 - 17, 2024, Wuhan, China.
  • Invited talk, “Artificial Intelligence and Brain-computer Interface for Lower Limb Rehabilitation,” at the Singapore Rehabilitation Conference, 9 Nov 2024, Singapore.
  • Invited talk “AI for Mental Health,” at the Inaugural Medical Artificial Intelligence and Data Science (MEDS) Seminar, LKC School of Medicine, 4 Sept 2024, Singapore
  • Invited talk “Geometric deep learning for motor brain-computer interface”, at the 2024 IEEE International Winter Conference on Brain-Computer Interface, 26-28 Feb 2024, Korea 
  • Keynote speech “Brain-Computer Interface in Stroke Rehabilitation and Motor Decoding Algorithms”, the 1st Brain-Computer Interface and Rehabilitation Forum, 25-26 Nov, 2023, Shanghai.
  • Invited talk “Decoding brain activity – pushing the technical frontiers”, RehabWeek’23, 24 Sep 2023, Singapore.
  • Invited talk “What can BCI do for stroke patients”, RehabWeek’23, 26 Sep 2023, Singapore.
  • Invited talk “Research and development of intelligent Brain-Computer Interfaces”, at the 12th China Intelligence Industry Summit, 18 Sep 2023 (online).
  • Keynote speech “Geometric Deep Learning for Brain-Computer Interfaces”, at the 4th International Workshop on Neural Engineering and Rehabilitation, 6-8 Aug, 2023, Chengdu, China.
  • Invited talk “Brain-Computer Interface-based Gait Prediction for Lower-Limb Stroke Rehabilitation”, at the 11th IEEE International Winter Conference on Brain-Computer Interface, February 20-22, 2023, Korea.
  • Keynote speech “Brain-Computer Interface Facilitates the Brain Function Enhancement and Restoration”, at the 6th International Conference on Disability & Rehabilitation, Riyadh, Saudi Arabia, 4-6 December 2022.
  • Keynote Speech “Non-invasive Brain-Computer Interfaces Facilitate the Enhancement and Restoration of Brain Functions”, at the International Conference of Human Performance Modelling and Augmentation, 5-6 Nov, 2022, Beijing (Online)
  • Invited talk “Brain-Computer Interface-based Gait Prediction for Lower-Limb Stroke Rehabilitation”, at the 7th Singapore Rehabilitation Conference, 8-9 October, Singapore, 2022 (Online)
  • Invited talk “Non-invasive Brain-Computer Interfaces for Enhancement or Restoration of Brain Functions”, at the International Workshop on Neurotechnologies for Mass Populations, 16 Sep 2022, Shanghai (Online).
  • Keynote speech “Emotion Recognition: A Piece of the Jigsaw Puzzle of a Holistic Rehabilitation System”, at the 3rd International Workshop on Neural Engineering & Rehabilitation, 9-11 May 2022 (Online)
  • Invited talk “Motor Imagery Decoding & Interpretations with Deep Neural Networks”, at the Waterloo International Workshop on Neural Engineering and Rehabilitation, 10 Jul, 2021 (online)
  • Invited talk “AI in Medicine and Healthcare”, at the National Health Group Eye Institute (NHGEI) Research Round, 1 July 2021 (online)
  • Invited talk “MI Decoding & Interpretations with Deep Neural Networks”, University of Bath CENTAUR Webinar Series: Brain-Computer Interfaces, 21 Jun 2021 (online)
  • Invited talk “Artificial Intelligence in Medicine and Healthcare”, Academy of Medicine Singapore, 26 Feb, 2021 (online)


Dr. Neethu ROBINSON

Program Manager
Senior Research Fellow

Research Interests:

  • Brain Computer Interfaces
  • Neural Signal Processing
  • Deep Learning and Pattern Recognition

Email: nrobinson@ntu.edu.sg

 

 

Aung Aung PHYO WAI

Research Associate

Research Interests:

  • Brain Computer Interfaces
  • Biomedical Signal Processing
  • Machine Learning

Email: apwaung@ntu.edu.sg 

Publications

  1. Shurui Li, Liming Zhao, Chang Liu, Jing Jin, Cuntai Guan, “Self-distillation with beta label smoothing-based cross-subject transfer learning for P300 classification,” Pattern Recognition, Mar 2025, DOI: 10.1016/j.patcog.2024.111114. Link
  2. Bradley J. Edelman, Shuailei Zhang, Gerwin Schalk,Peter Brunner, Gernot Müller-Putz, Cuntai Guan, and Bin He, "Non-invasive Brain-Computer Interfaces: State of the Art and Trends," IEEE Reviews in Biomedical Engineering (R-BME),  Jan 2025, 18, pp 26-49. ViewPDF
  3. Shurui Li, Ian Daly, Cuntai Guan, Andrzej Cichocki, Jing Jin, “Inter-participant Transfer Learning with Attention based Domain Adversarial Training for P300 Detection,” Neural Networks, Dec 2024, 106655. Link
  4. Ce Ju and Cuntai Guan, “Graph Neural Networks on SPD Manifolds for Motor Imagery Classification: A Perspective from the Time-Frequency Analysis”, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), Dec 2024, 35(12), pp 17701-17715. ViewPDF
  5.  Yi Ding, Yong Li, Hao Sun, Rui Liu, Chengxuan Tong, Chenyu Liu, Xinliang Zhou, Cuntai Guan, "EEG-Deformer: A Dense Convolutional Transformer for Brain computer Interfaces," IEEE Journal of Biomedical and Health Informatics, Nov 2024, DOI: 10.1109/JBHI.2024.3504604. ViewPDF 
  6.  Zhao Feng, Cuntai Guan, Ruifeng Zheng, Yu Sun, “STARTS: A self-adapted spatio-temporal framework for automatic E/MEG source imaging,” IEEE Transactions on Medical Imaging, Oct 2024, DOI: 10.1109/TMI.2024.3483292. Link 
  7. Fenqi Rong, Banghua Yang, Cuntai Guan, “Decoding Multi-Class Motor Imagery from Unilateral Limbs Using EEG Signals,” IEEE Transactions on Neural Systems and Rehabilitation Engineering (TNSRE), Sep 2024, 32, pp 3399-3409. ViewPDF 
  8. Yi Ding, Su Zhang, Chuangao Tang, Cuntai Guan, “MASA-TCN: Multi-anchor Space-aware Temporal Convolutional Neural Networks for Continuous and Discrete EEG Emotion Recognition," IEEE Journal of Biomedical and Health Informatics (J-BHI), Jul 2024, 28 (7), pp 3953-3964. ViewPDF
  9. Yi Ding, Neethu Robinson, Chengxuan Tong, Qiuhao Zeng, Cuntai Guan, “LGGNet: Learning from Local-Global-Graph Representations for Brain-Computer Interface”, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), Jul 2024, 35(7), 9773-9786. ViewPDF
  10. Hannah S Pulferer, Cuntai Guan, and Gernot R Müller-Putz, “Investigating Multilevel Cognitive Processing within Error-free and Error-prone Feedback Conditions in Executed and Observed Car Driving,” Frontiers Human Neuroscience, Jun 2024, DOI: 10.3389/fnhum.2024.1383956. ViewPDF
  11. Hao Sun, Yi Ding, Jianzhu Bao, Ke Qin, Chengxuan Tong, Jing Jin, Cuntai Guan, “Leveraging Temporal Dependency for Cross-subject-MI BCIs by Contrastive Learning and Self-attention,” Neural Networks, Jun 2024, DOI: 10.1016/j.neunet.2024.106470. Link
  12. Huazhang Guo, Yuhao Lu, Zhendong Lei, Hong Bao, Mingwan Zhang, Zeming Wang, Cuntai Guani, Bijun Tang, Zheng Liu, Liang Wang, “Machine learning-guided realization of full-color high-quantum-yield carbon quantum dots,” Nature Communications, Jun 2024,15:4843, DOI: 10.1038/s41467-024-49172-6. ViewPDF
  13. Wei Zhang, Muyun Jiang, Kok Ann Colin Teo, Raghavan Bhuvanakantham, LaiGuan Fong, Wei Khang Jeremy Sim, Zhiwei Guo, Chuan Huat Vince Foo, Rong Hui Jonathan Chua, Parasuraman Padmanabhan, Victoria Leong, Jia Lu, Balázs Gulyás, Cuntai Guan, “Revealing the Spatiotemporal Brain Dynamics of Covert Speech compared with Overt Speech: A Simultaneous EEG-fMRI Study,” NeuroImage, Jun 2024, DOI: 10.1016/j.neuroimage.2024.120629. ViewPDF
  14. Gerwin Schalk, Peter Brunner, Brendan Z. Allison, Surjo R. Soekadar, Cuntai Guan, Tim Denison, Jörn Rickert, Kai J. Miller, “Translation of Neurotechnologies,” Nature Reviews Bioengineering, May 2024, DOI: 10.1038/s44222-024-00185-2. Link
  15. Chengxuan Tong, Yi Ding, Zhuo Zhang, Haihong Zhang, Kevin JunLiang Lim, and Cuntai Guan, “TASA: Temporal Attention with Spatial Autoencoder Network for Odor-induced Emotion Classification Using EEG,” IEEE Transactions on Neural Systems and Rehabilitation Engineering (TNSRE), May 2024, DOI: 10.1109/TNSRE.2024.3399326. ViewPDF 
  16. Shangen Zhang, Hongyan Cui, Yong Li, Xiaogang Chen, Xiaorong Gao, Cuntai Guan, “Improving SSVEP-BCI performance through repetitive anodal tDCS-based neuromodulation: insights from fractal EEG and brain functional connectivity,” IEEE Transactions on Neural Systems and Rehabilitation Engineering (TNSRE), April 2024, DOI: 10.1109/TNSRE.2024.3389051. ViewPDF
  17. Han Wei Ng, Cuntai Guan, “Subject-Independent Meta-Learning Framework Towards Optimal Training of EEG-based Classifiers,” Neural Networks, Jan 2024, DOI: 10.1016/j.neunet.2024.106108. ViewPDF
  18. Rui Liu, Yuanyuan Chen, Anran Li, Yi Ding, Han Yu, Cuntai Guan, “Aggregating Intrinsic Information to Enhance BCI Performance through Federated Learning”, Neural Networks, April 2024, DOI: 10.1016/j.neunet.2024.106100. ViewPDF
  19. Zhiya Wang, Xue Pan, Zhen Mei, Zhifei Xu, Yudan Lv, Yuan Zhang, Cuntai Guan, "ECGAN-Assisted ResT-Net Based on Fuzziness for OSA Detection," IEEE Transactions on Biomedical Engineering (TBME), April 2024, DOI: 10.1109/TBME.2024.3378508. ViewPDF
  20. Mengru Xu, Zhao Feng, Sujie Wang, Hui Gao, Jiaye Cai, Biwen Wu, Huaying Cai, Yi Sun, Cuntai Guan, "Machine learning technique reveals intrinsic EEG connectivity characteristics of patients with mild stroke during cognitive task performing," IEEE Transactions on Cognitive and Developmental Systems, Feb 2024, 16(1), pp 232-242. ViewPDF
  21. Rui Liu, Pengwei Xing, Zichao Deng, Anran Li, Cuntai Guan, Han Yu, "Federated Graph Neural Networks: Overview, Techniques and Challenges," IEEE Transactions on Neural Networks and Learning Systems (TNNLS), Jan 2024, DOI: 10.1109/TNNLS.2024.3360429. ViewPDF
  22. Aarthy Nagarajan, Neethu Robinson, Kai Keng Ang, Karen Sui Geok Chua, Effie Chew, and Cuntai Guan, “Transferring a deep learning model from healthy subjects to stroke patients in a motor imagery brain-computer interface”, Journal of Neural Engineering (JNE), Jan 2024, DOI: 10.1088/1741-2552/ad152f. ViewPDF
  23. Ce Ju, Cuntai Guan, "Tensor-CSPNet: A Novel Geometric Deep Learning Framework for Motor Imagery Classification", IEEE Transactions on Neural Networks and Learning Systems (TNNLS), Dec 2023, 34 (12), 10955 - 10969. ViewPDF
  24. Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee-Keong Kwoh, Xiaoli Li, Cuntai Guan, “Self-supervised Contrastive Representation Learning for Semi-supervised Time-Series Classification”, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), Dec 2023, 45(12), 15604 - 15618. ViewPDF
  25. Han Wei Ng and Cuntai Guan, “Deep Unsupervised Representation Learning for Feature-Informed EEG Domain Extraction”, IEEE Transactions on Neural Systems and Rehabilitation Engineering (TNSRE), Nov 2023, DOI: 10.1109/TNSRE.2023.3339179. ViewPDF
  26. Rosary Yuting Lim, Kai Keng Ang, Effie Chew, and Cuntai Guan, “Motor imagery with transcranial alternating current stimulation in a holistic approach towards patient rehabilitation: on bridging motor and cognitive welfare”, Brain Sciences, Nov 2023, 13(11), 1584, pp1-19. ViewPDF
  27. Erico Tjoa, Hong Jing Khok, Tushar Chouhan, Cuntai Guan, “Enhancing the Confidence of Deep Learning Classifiers via Interpretable Saliency Maps”, Neurocomputing, October 2023, 126825, DOI: 10.1016/j.neucom.2023.126825. ViewPDF
  28. Zhao Feng, Sujie Wang, Linze Qian, Mengru Xu, Kuijun Wu, Ioannis Kakkos, Cuntai Guan, Yu Sun, “μ-STAR: A novel framework for spatio-temporal M/EEG source imaging optimized by microstates”, NeuroImage, Sep 2023, 120372, DOI: 10.1016/j.neuroimage.2023.120372. ViewPDF
  29. Mohamed Elgendi, Wenshan Wu, Cuntai Guan, Carlo Menon, “Revolutionizing Smartphone Gyrocardiography for Heart Rate Monitoring”, Frontiers in Cardiovascular Medicine, Aug 2023, DOI: 10.3389/fcvm.2023.1237043. ViewPDF
  30. Yi Ding, Neethu Robinson, Qiuhao Zeng and Cuntai Guan, “TSception: Capturing Temporal Dynamics and Spatial Asymmetry from EEG for Emotion Recognition”, IEEE Transactions on Affective Computing, July-Sept 2023, 14 (3), pp 2238 - 2250. ViewPDF
  31. Tao Xu, Hongtao Wang, Guanyong Lu, Feng Wan, Mengqi Deng, Peng Qi, Anastasios Bezerianos, Cuntai Guan, Yu Sun, "E-Key: an EEG-Based Biometric Authentication and Driving Fatigue Detection System," IEEE Transactions on Affective Computing (TAFFC), Dec 2021, May 2023, 14 (2), pp 864-877. ViewPDF
  32. Jianbo Chen, Yangsong Zhang, Yudong Pan, Peng Xu, Cuntai Guan, “A transformer-based deep neural network model for SSVEP classification,” Neural Networks, May 2023, DOI: 10.1016/j.neunet.2023.04.045. ViewPDF
  33. Ziyuan Zhao, Fangcheng Zhou, Kaixin Xu, Zeng Zeng, Cuntai Guan, S. Kevin Zhou, “LE-UDA: Label-efficient unsupervised domain adaptation for medical image segmentation”, IEEE Transactions on Medical Imaging (TMI), Mar 2023, 42 (3), pp 633-646. ViewPDF
  34. Chuanhao Zhang, Emil Jovanov, Hongen Liao, Yuan-Ting Zhang, Benny Lo, Yuan Zhang, Cuntai Guan, “Video Based Cocktail Causal Container for Blood Pressure Classification and Blood Glucose Prediction”, IEEE Journal of Biomedical and Health Informatics (J-BHI), Feb 2023, 27(2), pp1118-1128. 
  35. Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee-Keong Kwoh, Xiaoli Li, Cuntai Guan, “ADAST: Attentive Cross-domain EEG-based Sleep Staging Framework with Iterative Self-Training”, IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI), Feb 2023, 7(1), pp 210-221. ViewPDF
  36. Erico Tjao and Cuntai Guan, “Self Reward Design with Fine-grained Interpretability”, Scientific Reports, Jan 2023, 13, 1638. DOI: 10.1038/s41598-023-28804-9. ViewPDF
  37. Choon Guan Lim, Chui Pin Soh, Shernice Shi Yun Lim, Daniel Shuen Sheng Fung, Cuntai Guan, Tih Shih Lee, “Home-based brain-computer interface attention training program for Attention Deficit Hyperactivity Disorder: A feasibility trial”, Child and Adolescent Psychiatry and Mental Health, Jan 2023, 17 (15). DOI: 10.1186/s13034-022-00539-x. ViewPDF
  38. Aarthy Nagarajan, Neethu Robinson, Cuntai Guan, “Relevance based Channel Selection in Motor Imagery Brain-Computer Interface”, Journal of Neural Engineering (JNE), Jan 2023, 20, 016024, DOI: 10.1088/1741-2552/acae07. ViewPDF
  39. Tushar Chouhan, Melissa Black, Sonya Girdler, Sven Bölte, Tele Tan, and Cuntai Guan, “Altered Task Induced Functional Brain Networks and Small-World Properties in Autism”, Frontiers in Psychiatry,Jan 2023, DOI: 10.3389/fpsyt.2022.1039820. ViewPDF
  40. Weijie Fei, Luzheng Bi, Jiarong Wang, Shengchao Xia, and Cuntai Guan, “Effects of Cognitive Distraction on Upper Limb Movement Decoding from EEG Signals”, IEEE Transactions on Biomedical Engineering (TBME), Jan 2023, 70 (1), pp 166-174. ViewPDF
  1. Ravikiran Mane, Kai Keng Ang, Cuntai Guan, “Brain Computer Interface for Stroke Rehabilitation”, in book Handbook of Neuroengineering, Springer, 2021.
  2. Xinyang Li, Cuntai Guan, Huijuan Yang, “Discriminative Learning of Connectivity Pattern of Motor Imagery EEG”, in book Signal Processing and Machine Learning for Brain-Machine Interfaces, IET, 2018.
  3. Xin Yi Lee, Eleni Koukouna, Choon Guan Lim, Cuntai Guan, Tih Shih Lee, Daniel Shuen Sheng Fung, “Can We Play with ADHD? An Alternative Game-Based Treatment for Inattentive Symptoms in Attention-Deficit Hyperactivity Disorder”, in book Subconscious Learning via Games and Social Media, pp 69-86, Springer Singapore, 2015.
  4. Chun Siong Lee, Chee Kong Chui, Cuntai Guan, Pui Wai Eu, Bhing Leet Tan, Joseph Jern-Yi Leong, “Integrating EEG Modality in Serious Games for Rehabilitation of Mental Patients”, in book Simulations, Serious Games and Their Applications, pp51-68, Springer Singapore, 2014.
  5. Yaozhang Pan, Cuntai Guan, How-Lung Eng, Shuzhi Sam Ge, Yen ling Ng, Derrick Wei Shih Chan, “Quantitative evaluation of interictal high frequency oscillations in scalp EEGs for epileptogenic region localization”, in book Knowledge Engineering and Management, pp 419-427, Springer, Berlin, Heidelberg, 2014.
  6. Cuntai Guan, Kai Keng Ang, et al. “State-of-the-Art in BCI Research: BCI Award 2010”, in book Recent Advances in Brain-Computer Interface Systems, ISBN: 978-953-307-175-6, InTech, pp193-222, 2011.
  7. Yuanqing Li, Kai Keng Ang, Cuntai Guan, “Digital Signal Processing and Machine Learning”, in book Brain-Computer Interfaces - Revolutionizing Human-Computer Interaction, ISBN: 978-3-642-02090-2, Springer, 2010.
  1. Ziyuan Zhao, Fen Fang, Xulei Yang, Qianli Xu, Cuntai Guan, and S. Kelvin Zhou, “See, Predict, Plan: Diffusion for Procedure Planning in Robotic Surgical Videos,” 27th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 6-10 Oct 2024 Morocco
  2. Ruixuan Zhang, Wenhuan Lu, Cuntai Guan, Jei Gao, Xi Wei, and Xuewei Li, “SHAN: Shape Guided Network for Thyroid Nodule Ultrasound Cross-Domain Segmentation,” 27th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 6-10 Oct 2024 Morocco.
  3. Han Wei Ng, Cuntai Guan, “Unsupervised Few-Shot Adaptive Re-Learning for EEG-based Motor Imagery Classification,” 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Oct 6-10, Sarawak, Malaysia.
  4. Han Wei Ng, Cuntai Guan, “Self-Selecting Semi-Supervised Transformer-Attention Convolutional Network for Four Class EEG-Based Motor Imagery Decoding,” 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 14-18, 2024, Abu Dhabi, UAE.
  5. Ziyuan Zhao, Zhi Qing Ng, Zhongyao Cheng, Jiahao Wang, Xulei Yang, Hanry Yu, Cuntai Guan, “Dual Prototypical Self-Supervised Learning for One-Shot Medical Image Segmentation,” 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, Florida, USA, July 15-19, 2024.
  6. Ziyuan Zhao, Renjun Cai, Kaixin Xu1, Zhengji Liu, Xulei Yang, Jun Cheng, Cuntai Guan, “Multi-dataset Collaborative Learning for Liver Tumor Segmentation,” 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, Florida, USA, July 15-19, 2024.
  7. Yuting Tang, Neethu Robinson, Cuntai Guan, “Hand Joint Reconstruction from Electroencephalography Signals with Deep Learning,” 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, Florida, USA, July 15-19, 2024.
  8. Xiaohao Lin, Emadeldeen Eldele, Zhenghua Chen, Min Wu, Han Wei Ng, Cuntai Guan, “Bi-hemisphere Interaction Convolutional Neural Network for Motor Imagery Classification,” 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, Florida, USA, July 15-19, 2024.
  9. Rosary Yuting Lim, Muyun Jiang, Kai Keng Ang, Xiaohao Lin and Cuntai Guan, “Brain-Computer-Brain system for individualized transcranial alternating current stimulation with concurrent EEG recording: a healthy subject pilot study,” 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, Florida, USA, July 15-19, 2024. 
  10. Kairui Hu, Ming Yan, Wen Haw Chong, Yong Keong Yap, Cuntai Guan, Joey Tianyi Zhou, Ivor W Tsang, “Ladder-Of-Thought: Using Knowledge as Steps to Elevate Stance Detection,” the IEEE World Congress on Computational Intelligence (WCCI), Yokohama, Japan, 30 June - 5 July 2024.
  11. Han Wei Ng, Kavitha Thomas, Neethu Robinson, Aung Aung Phyo Wai, Leran Jenny Liang, Nishka Khendry, Aarthy Nagarajan and Cuntai Guan, “CASTNet: Cycle-Consistent Attention-Based Network for Decoding Open/Close Hand Movement Attempts Using EEG,” the IEEE World Congress on Computational Intelligence (WCCI), Yokohama, Japan, 30 June - 5 July 2024.
  12. Ce Ju, Reinmar J Kobler, Liyao Tang, Cuntai Guan, Motoaki Kawanabe, “Deep Geodesic Canonical Correlation Analysis for Covariance-Based Neuroimaging Data”, the Twelfth International Conference on Learning Representations (ICLR), May 7- 11, 2024, Vienna, Austria.
  13. Han Wei Ng and Cuntai Guan, “Efficient Representation Learning for Inner Speech Domain Generalization”, The 20th International Conference on Computer Analysis of Images and Patterns, 26-28 September, 2023, Main Conference, Limassol, Cyprus.
  14. Ziyuan Zhao, Peisheng Qian, Xulei Yang, Zeng Zeng, Cuntai Guan, Wai Leong Tam, Xiaoli Li, “SemiGNN-PPI: Self-Ensembling Multi-Graph Neural Network for Efficient and Generalizable Protein-Protein Interaction Prediction”, The 31st International Joint Conference on Artificial Intelligence (IJCAI), 19-25 Aug 2023, Macao, China.
  15. Yi Ding and Cuntai Guan, “GIGN: Learning Graph-in-graph Representations of EEG Signals for Continuous Emotion Recognition,” 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Conference (EMBC), 24-27 Jul 2023, Sydney.
  16. Xi Fu and Cuntai Guan, “Gait Pattern Recognition Based on Supervised Contrastive Learning Between EEG and EMG,” 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Conference (EMBC), Jul 2023, Sydney.
  17. Chengxuan Tong, Yi Ding, Kevin Lim, Cuntai Guan, “MTDN: Learning Multiple Temporal Dynamics representation for Emotional Valence Classification with EEG,” 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Conference (EMBC), 24-27 Jul 2023, Sydney.
  18. Ce Ju, Reinmar Josef Kobler, Cuntai Guan,“Score-based Data Generation for EEG Spatial Covariance Matrices: Towards Boosting BCI Performance”, 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Conference (EMBC), 24-27 Jul 2023, Sydney.
  19. Hannah Sophia Pulferer, Gernot Müller-Putz, Cuntai Guan, “Continuous erroneous feedback processing during deviation from the road within a 2D steering task”, 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Conference (EMBC), 24-27 Jul 2023, Sydney.
  20. Shuailei Zhang, Dezhi Zheng, Ning Tang, Effie Chew, Rosary Yuting Lim, Kai Keng Ang, Cuntai Guan, "Online Adaptive CNN: a Session-to-session Transfer Learning Approach for Non-stationary EEG," IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2022), 4-7 Dec 2022 (IEEE Brain Best Paper Award).

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