Centre for Brain-Computing Research (CBCR)

Centre for Brain-Computing Research (CBCR)Centre for Brain-Computing Research (CBCR)Centre for Brain-Computing Research (CBCR)
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Centre for Brain-Computing Research (CBCR)

Centre for Brain-Computing Research (CBCR)Centre for Brain-Computing Research (CBCR)Centre for Brain-Computing Research (CBCR)
  • Home
  • Research
  • Team
  • Publications
  • Patents
  • Updates
  • Vacancies

Overview

Artificial intelligence and big data have changed the world in an unprecedented way. Brain-computer interface (BCI) technology will be encompassed in the next wave of artificial intelligence and data science to create new digital health solutions for mental health and brain disorders.


Stroke is the top cause of long-term disability in Singapore (63% of stroke patients have some disability at three months) and the burden of stroke is expected to increase dramatically owing to Singapore’s rapidly ageing population and stroke risk factors. Current gold-standard interventions for stroke are either drug-based or therapies limited by the target functionalities. State-of-the-art research on BCI-based stroke rehabilitation and cognitive training primarily focus on upper limb rehabilitation and motor function only. Little research has been done in incorporating motor, cognition, and emotion training in an integrated solution for treating brain and boost the patient outcome.


In this programme, we propose to develop a digital healthtech solution to address the healthcare challenges: Next-Generation Brain-Computer-Brain Platform – A Holistic Solution for the Restoration & Enhancement of Brain Functions (NOURISH). The NOURISH programme proposes to develop the best-in-class, holistic solution for the restoration and enhancement of motor, cognitive and emotional functions in one system. The NOURISH platform would offer a non-drug based, cost-effective and clinically feasible solution for effective stroke rehabilitation and mood therapy.


The programme will capitalize on powerful and advanced machine learning algorithms including deep learning, deep transfer learning and deep reinforcement learning for source-space, multi-view and multimodal brain state decoding as well as neuroimaging-based intervention efficacy prediction and monitoring.


The integrated BCB features AI-powered digital health technology for high-resolution brain signal decoding, intelligent neurofeedback and personalized treatment. It would be clinically validated for stroke therapy and can be extended to other use-cases including mental health and brain training. We anticipate that the research outcomes from this project will generate significant scientific, societal and potentially economic impact.

Programme Goals

Programme Organization

The programme is organized into 3 Work Packages (WPs) namely (1) Motor, Cognitive and Affect Brain-Computer- Brain (BCB) systems (2) Unified BCB platform for ML and Data Analytics (3) Clinical validation and optimization. The WPs collectively work towards the goal of the development of optimal deep-learning (DL) and AI-powered digital health technology to decode multiple brain states and to uncover neurological mechanisms of recovery.


WP1leads the development of proposed therapy systems that encompass multiple domains of brain functionality, namely, upper extremity (UE), fingers, and lower extremity (LE) motor, cognition, and affect. This WP also studies the impact of multimodal feedback, including VR, mechanical, and electrical stimulation in the therapy. 


WP2is the key data processing and technology platform development WP. The technical innovations include the extensive application of deep learning including deep reinforcement learning, generative adversarial networks, deep transfer learning, deep source space representation learning, multi-view and multimodal brain state decoding with the collective goal of accurate decoding of multiple brain states and interpretable network representations of brain activity. The reasearchalso introduces a secure data management and analysis framework for clinical data powered by federated machine learning to enable multi-domain data analysis as well as multi-view transfer learning for stratification and personalization of treatment plans. WP2 also conducts systematic evaluation of motivation factors that facilitate the BCB therapy. 


WP3, the clinical work package, leads the proposed clinical trials that comprise of extensive longitudinal study multimodal assessment protocol involving various demographic, clinical and neurological variables to achieve an all-inclusive holistic understanding of the brain as well as validation of BCB in LE motor, cognition and integrated therapy. WP3 also leads research to optimize the clinical trials (dosage and combination of therapies) based on the Curate.AI method. 

Projects & Team

Research Outcomes

Research outcomes achieved are highest results reported in Motor Imagery classification, Cognition and Emotion Recognition and are significantly higher than the performance reported in state-of-the-art approaches


Upper Limb Motor classification

  • Our team proposed a novel FBCNet reported motor imagery (MI) classification accuracy significantly highercompared to state-of-the-art approaches.
  • Our team proposed a novel relevance based channel selection approach optimized the Deep CNN algorithm and achieved the best subject-independent performance with only a small subset of available EEG channels.
  • Our team proposed a novel Tensor CSPNet that fully captures the temporo-spatio-frequency patterns using existing deep neural networks on SPD manifolds for MI-EEG classification
  • Our team proposed a novel Graph CSPNet that features manifold-valued graph convolutional techniques to capture time-frequency EEG features and report best accuracy in five public MI-EEG datasets.
  • Our team introduced a novel Transfer Learning approach to employ a MI-BCI pre-trained on healthy subjects to detect MI in stroke patients and reported a significant improvement in classification.
  • Our team proposed an online adaptive CNNto address the non-stationarity in multi-session EEG by progressively updating a subject-specific model and reported best performance across two neurorehabilitation datasets. This work received IEEE Brain Best Paper Award in IEEE SSCI 2022.


Lower Limb motor decoding

  • Our team proposed a Multi-model Attention Network achieved best gait prediction from EEG with a high Pearson’s correlation coefficient, significantly higher than state-of-the-art approaches.
  • Our team proposed a multimodal training strategy based on supervised contrastive learning for reconstruction of gait patterns and reported the best performance compared to existing single-modal approaches.


Cognitive state classification

  • Our team proposed a novel LGGNet and reported the best classification in four types of cognitive classification tasks, namely the attention, fatigue, emotion, and preference among 3 benchmarking datasets.


Emotion recognition

  • Our team proposed a novel TSception reported significantly higher accuracy in arousal, valence and liking classification than state-of-the-art methods. 
  • Our team proposed a novel cascade convolutional neural network – temporal convolutional network and a visual-to-EEG cross modal knowledge distillation that significantly improved continuous predictionof emotion using EEG.
  • Our team proposed a GIGN based on Spatial graphs in a temporal graph representation of EEG and reported best regression performance in continuous emotion recognition.
  • Completed prototypes features novel BCB designs to enhance efficacy of motor training, cognition training, emotion regulation, affective music generation and emotion priming for motor training.
  • Distal Upper Extremity motor training using HandyRehab and EEG-BCI 
  • Decoding EEG during Continuous Movement of distal Upper Extremity
  • BCI for lower limb and upper extremity motor training and ankle motor intent detection
  • Gait prediction from EEG
  • Deep learning for EEG based emotion profiling using generated affective music
  • Classical and Retro-pop AMGS
  • Federated learning prototypes
  • DTx platform for cognition training

Publications

  1. 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. (IF: 9.657)
  2. Rui Liu, Yuanyuan Chen, AnranLi, Yi Ding, Han Yu, Cuntai Guan, “Aggregating Intrinsic Information to Enhance BCI Performance through Federated Learning”, Neural Networks, Jan 2024, DOI: 10.1016/j.neunet.2024.106100. (IF: 9.657)
  3. Aarthy Nagarajan, Neethu Robinson, K. K. Ang, Karen Sui Geok Chua, E. 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), December 2023, DOI: 10.1088/1741-2552/ad152f. (IF: 5.043)
  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), September 2023. (Impact factor 14.255) ​
  5. Yi Ding, Neethu Robinson, Qiuhao Zeng, and Cuntai Guan, “LGGNet: Learning from Local Global-Graph Representations for Brain-Computer Interface”,  IEEE Transactions on Neural Networks and Learning Systems (Minor Revision), Nov 2022. (Impact factor 14.255) ​
  6. Nagarajan Aarthy, Neethu Robinson, and Cuntai Guan,  “Relevance based Channel Selection in MI-BCI”, Journal Of Neural Engineering (Major Revision), Nov 2022. (Impact factor 5.043) ​
  7. 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), June, 2022, DOI: 10.1109/TNNLS.2022.3172108.  (IF: 14.255)
  8. Yi Ding, Neethu Robinson, Su Zhang, Qiuhao Zeng and Cuntai Guan, “TSception: Capturing Temporal Dynamics and Spatial Asymmetry from EEG for Emotion Recognition”, IEEE Transactions on Affective Computing (TAFFC), April 2022. (IF: 10.506)
  9. Su Zhang, Chuangao Tang, Cuntai Guan, “Visual-to-EEG cross-modal knowledge distillation for continuous emotion recognition”, Pattern Recognition, June 2022, 108833, DOI: 10.1016/j.patcog.2022.108833.​ (IF: 7.74)
  10. Rajan Kashyap, S. Bhattacharjee, R. Arumugam, Rose Dawn Bharath, Kaviraja Udupa, Kenichi Oishi, John E. Desmond, S.H. Annabel Chen, Cuntai Guan “Focality Oriented Selection of Current Dose for Transcranial Direct Current Stimulation”, Journal of Personalized Medicine, Sept 2021, 11 (940), pp 1-14. ​ (IF: 4.453)
  11. Neethu Robinson, Ravikiran Mane, Tushar Chouhan, Cuntai Guan, “Emerging Trends in BCI-Robotics for Motor Control and Rehabilitation”, Current Opinion in Biomedical Engineering (COBME), Oct 2021, 20:100354.  (IF: 4.164)
  12. Qianyu Li, Jiale Yao, XiaoliTang, Han Yu, Siyu Jiang, HaizhiYang & HengjieSong. Capsule neural tensor networks with multi-aspect information for few-shot knowledge graph completion. Neural Networks, vol. 164, pp. 323–334, Elsevier (2023). (IF: 9.657)
  13. Hongli Bian, Jie Tian, JialiangYu & Han Yu. Bayesian co-evolutionary optimization based entropy search for high-dimensional many-objective optimization. Knowledge-Based Systems, Elsevier (2023). (IF: 8.139)
  14. Chang'an Yi, HaotianChen, YonghuiXu, HuanhuanChen, Yong Liu, Haishu Tan, YuguangYan & Han Yu. Multi-component adversarial domain adaptation: A general framework. IEEE Transactions on Neural Networks and Learning Systems, IEEE (2023). (IF: 14.255) 
  15. Yuxin Shi, Han Yu & Cyril Leung. Towards fairness-aware federated learning. IEEE Transactions on Neural Networks and Learning Systems, IEEE (2023). (IF: 14.255)
  16. Chang Liu & Han Yu. AI-empowered persuasive video generation: A survey. ACM Computing Surveys, ACM (2023). (IF: 16.062)
  17. Lingjuan Lyu, Han Yu, XingjunMa, Chen Chen, LichaoSun, Jun Zhao, Qiang Yang & Philip S. Yu. Privacy and robustness in federated learning: Attacks and defenses. IEEE Transactions on Neural Networks and Learning Systems, IEEE (2022). (IF: 14.255)
  18. Haoran Shi, YonghuiXu, YaliJiang, Han Yu & Lizhen Cui. Efficient asynchronous multi-participant vertical federated learning. IEEE Transactions on Big Data, IEEE (2022). (IF: 4.271)
  19. Xavier Tan, Wei Chong Ng, Wei Yang Bryan Lim, Zehui Xiong, Dusit Niyato& Han Yu. Reputation-aware federated learning client selection based on stochastic integer programming. IEEE Transactions on Big Data, IEEE (2022). (IF: 4.271)
  20. Pengwei Xing, SongtaoLu, LingfeiWu & Han Yu. BiG-Fed: Bilevel optimization enhanced graph-aided federated learning. IEEE Transactions on Big Data, IEEE (2022). (IF: 4.271)
  21. Siwei Feng, BoyangLi, Han Yu, Yang Liu & Qiang Yang. Semi-supervised federated heterogeneous transfer learning. Knowledge-Based Systems, Elsevier (2022). (IF: 8.139)
  22. Xiaohu Wu & Han Yu. MarS-FL: Enabling competitors to collaborate in federated learning. IEEE Transactions on Big Data, IEEE (2022). (IF: 4.271)
  23. Zelei Liu, Yuanyuan Chen, Han Yu, Yang Liu & LizhenCui. GTG-Shapley: Efficient and accurate participant contribution evaluation in federated learning. ACM Transactions on Intelligent Systems and Technology, ACM (2022). (IF: 10.489)
  24. Xu Guo, Han Yu, BoyangLi, Hao Wang, Pengwei Xing, Siwei Feng, ZaiqingNie & ChunyanMiao. Federated learning for personalized humor recognition. ACM Transactions on Intelligent Systems and Technology, ACM (2022). (IF: 10.489)
  25. Yuan-Ai Xie, JiawenKang, Dusit Niyato, Nguyen Thi Thanh Van, Nguyen Cong Luong, ZhixinLiu & Han Yu. Securing federated learning: A covert communication-based approach. IEEE Network, IEEE (2022). (IF: 10.294)
  26. Alysa ZiyingTan, Han Yu, Lizhen Cui & QiangYang. Towards personalized federated learning. IEEE Transactions on Neural Networks and Learning Systems, doi:10.1109/TNNLS.2022.3160699, IEEE (2022). (IF: 14.255)
  27. Chen Chen, Lingjuan Lyu, Han Yu & Gang Chen. Practical attribute reconstruction attack against federated learning. IEEE Transactions on Big Data, doi:10.1109/TBDATA.2022.3159236, IEEE (2022). (IF: 4.271)
  28. Yuxin Zhang, Jindong Wang, YiqiangChen, Han Yu & Tao Qin. Adaptive memory networks with self-supervised learning for unsupervised anomaly detection. IEEE Transactions on Knowledge and Data Engineering, IEEE (2022). (IF: 9.235)
  29. Shangwei Guo, TianweiZhang, GuowenXu, Han Yu, Tao Xiang & Yang Liu. Byzantine-resilient decentralized stochastic gradient descent . IEEE Transactions on Circuits and Systems for Video Technology, IEEE (2021). (IF: 5.859)
  30. Shangwei Guo, TianweiZhang, GuowenXu, Han Yu, Tao Xiang & Yang Liu. Topology-aware differential privacy for decentralized image classification. IEEE Transactions on Circuits and Systems for Video Technology, IEEE (2021). (IF: 5.859)
  31. Raczkowska, M. N., Remus, A., Vijayakumar, S., Kwek, S. P., Lee, V. V., Tadeo, X., Ho, D. & Vellayappan, B. A. (2023). Mixed-methods clinical trial to evaluate the feasibility of the CURATE. AI optimised digital cognitive rehabilitation therapeutic (COR-Tx) in patients post brain radiotherapy. Journal of Clinical Oncology 41:16_suppl, TPS1615-TPS1615 
  32. Hsiao-Ju Cheng, … Helen Juan Zhou. Task-related brain functional network reconfigurations relate to motor recovery in chronic subcortical stroke, 2021, Scientific reports.
  33. Veldsman M*, Hsiao-Ju Cheng*, … Helen Juan Zhou. Degeneration of structural brain networks is associated with cognitive decline after ischaemic stroke. 2020, Brain Communications
  34. Agres, K. R., Dash, A., & Chua, P. (2023). AffectMachine-Classical: a novel system for generating affective classical music. Frontiers in Psychology, 14, 1158172. IF = 4.232
  35. Agres, K., Foubert, K., & Sridhar, S. (2021). Music Therapy During COVID-19: Changes to the Practice, Use of Technology, and What to Carry Forward in the Future. Front. Psychol. 12:647790. IF = 4.232

  1. Rajan Kashyap, S. Bhattacharjee, R. D Bharath, G. Venkatasubramanian, K. Udupa, S. Bashir, K. Oishi, J. E Desmond, SHA Chen, Cuntai Guan, “Variation of Cerebrospinal Fluid in Specific Regions Regulates Focality in Transcranial Direct Current Stimulation”, Frontiers in Human Neuroscience, Aug 2022. (IF: 3.169)
  2. JiehuangZhang & Han Yu. EID: Facilitating explainable AI design discussions in team-based settings. International Journal of Crowd Science, Tsinghua University Press (2022).
  3. JiehuangZhang, Ying Shu & Han Yu. Fairness in Design: A framework for facilitating ethical AI designs. International Journal of Crowd Science, Tsinghua University Press (2022).
  4. Yang Liu, AnbuHuang, Yun Luo, He Huang, Youzhi Liu, Yuanyuan Chen, LicanFeng, TianjianChen, Han Yu & Qiang Yang, Federated learning-powered visual object detection for safety monitoring. AI Magazine 42(2), AAAI Press (2021). (IF: 2.524)
  5. ZeleiLiu, Yuanyuan Chen, Yansong Zhao, Han Yu, Yang Liu, RenyiBao, JinpengJiang, ZaiqingNie, Qian Xu & Qiang Yang. CAreFL: Enhancing smart healthcare with contribution-aware federated learning. AI Magazine, AAAI Press (2023). (IF: 2.524)
  6. Remus, Alexandria, et al (2023). CURATE.AI COR-Tx platform as a digital therapy and digital diagnostic for cognitive function in brain tumour patients post-radiotherapy treatment: Protocol for a prospective mixed-methods feasibility clinical trial“ (accepted to BMJ open)
  7. Tan, S. B., Tan, J., Raczkowska, M. N., Lee, J. C. W., Rai, B., Remus, A., & Ho, D. (2023). Digital game-based interventions for cognitive training in healthy adults and adults with cognitive impairment: protocol for a two-part systematic review and meta-analysis. BMJ open, 13(5), e071059.
  8. Zhang, S., Ang, K. K., Zheng, D., Hui, Q., Chen, X., Li, Y., Tang, N., Chew, E., Lim, R. Y., & Guan, C. (2022). Learning EEG Representations with Weighted Convolutional Siamese Network: a Large Multi-session Post-stroke Rehabilitation Study. IEEE Trans. Neural Syst. Rehabil. Eng., 30, 2824 - 2833. 
  9. MengjiaoHu, … Helen Juan Zhou. Brain functional changes in stroke following rehabilitation using brain-computer interface-assisted motor imagery with and without tDCS: a pilot study, 2021, Frontiers in Human Neuroscience.
  10. Koh, E. Y., Cheuk, K. W., Heung, K. Y., Agres, K. R., & Herremans, D. (2022). MERP: A music dataset with emotion ratings and raters’profile information. Sensors, 23(1), 382. IF = 3.847

  1. 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.
  2. 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.
  3. 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.
  4. Shuailei Zhang, DezhiZheng, 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).
  5. Xi Fu, Liming Zhao, Cuntai Guan, “MATN: Multi-model Attention Network for Gait Prediction from EEG”, 2022 IEEE World Congress on Computational Intelligence (IJCNN), 18-23 July, 2022, Padua, Italy​
  6. Yi Ding, Nigel Wei Jun Ang, Aung Aung Phyo Wai, Cuntai Guan, “Learning Generalized Representations of EEG between Multiple Cognitive Attention Tasks”, 43rd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society (EMBC), October 31-November 4, 2021.​
  7. Vishnupriya R, Neethu Robinson, RamasubbaReddy M, Cuntai Guan, “Performance Evaluation of Compressed Deep CNN for Motor Imagery Classification using EEG”, 43rd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society (EMBC), October 31-November 4, 2021.​
  8. Aarthy Nagarajan, Neethu Robinson, Cuntai Guan, “Investigation on Robustness of EEG-based Brain-Computer Interfaces”, 43rd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society (EMBC), October 31-November 4, 2021​
  9. Aung Aung Phyo Wai, Tchen JeeErn, Cuntai Guan, “A Study of Visual Search based Calibration Protocol for EEG Attention Detection”, 43rd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society (EMBC), October 31-November 4, 2021.​
  10. Su Zhang, Ruyi An, Yi Ding and Cuntai Guan, “Continuous Emotion Recognition using Visual-audio-linguistic Information: A Technical Report for ABAW3”, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW): 3rd Workshop and Competition on Affective Behavior Analysis in-the-wild (ABAW), June 19-20, 2022, New Orleans, USA​
  11. N. Robinson, C. Guan, "Interpreting deep network representations of EEG", International BCI Meeting (vBCI), June 7 – 9, 2021.​
  12. Anran Li, HongyiPeng, Lan Zhang, Jiahui Huang, Qing Guo, Han Yu & Yang Liu, "FedSDG-FS: Efficient and Secure Feature Selection for Vertical Federated Learning," in Proceedings of the 2023 IEEE International Conference on Computer Communications (INFOCOM'23), 2023.
  13. Yizhou Chen, Guangda Huzhang, QingtaoYu, Hui Sun, Heng-Yi Li, Jingyi Li, Yabo Ni, AnxiangZeng, Han Yu & Zhiming Zhou, "Clustered Embedding Learning for Large-scale Recommender Systems," in Proceedings of the ACM Web Conference 2023 (WWW'23), 2023. 
  14. Yuxin Shi, ZeleiLiu, ZhuanShi & Han Yu, “Fairness-Aware Client Selection for Federated Learning,” in Proceedings of the 2023 IEEE International Conference on Multimedia and Expo (ICME’23), 2023. (Best Poster Award, KDDSG) 
  15. Anran Li, Yue Cao, Jiabao Guo, HongyiPeng, Qing Guo & Han Yu, "FedCSS: Joint Client-and-Sample Selection for Hard Sample-Aware Noise-Robust Federated Learning," in Proceedings of the 2024 ACM SIGMOD/PODS International Conference on Management of Data (SIGMOD'24), 2024. 
  16. Yizhou Chen, AnxiangZeng, QingtaoYu, KeruiZhang, YuanpengCao, KangleWu, Guangda Huzhang, Han Yu & Zhiming Zhou, "Recurrent Temporal Revision Graph Networks," in Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS'23), 2023.
  17. Xiaoli Tang & Han Yu, "Competitive-Cooperative Multi-Agent Reinforcement Learning for Auction-based Federated Learning," in Proceedings of the 32nd International Joint Conference on Artificial Intelligence (IJCAI'23), 2023. (Best Poster Runner-Up Award, KDDSG) 
  18. Yuanyuan Chen, ZichenChen, PengchengWu & Han Yu, "FedOBD: Opportunistic Block Dropout for Efficiently Training Large-scale Neural Networks through Federated Learning," in Proceedings of the 32nd International Joint Conference on Artificial Intelligence (IJCAI'23), 2023.
  19. Jihu Wang, YuliangShi, Han Yu, Xinjun Wang, ZhongminYan & Fanyu Kong, "Mixed-Curvature Manifolds Interaction Learning for Knowledge Graph-aware Recommendation," in Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'23), 2023.
  20. Xu Guo, BoyangLi & Han Yu, "Improving the Sample Efficiency of Prompt Tuning with Domain Adaptation," in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP'22), 2022.
  21. Xianshuai Cao, YuliangShi, Jihu Wang, Han Yu, Xinjun Wang & ZhongminYan, "Cross-modal Knowledge Graph Contrastive Learning for Machine Learning Method Recommendation," in Proceedings of the 30th ACM Multimedia Conference (ACM MM'22), 2022.
  22. Yanci Zhang & Han Yu, "Towards Verifiable Federated Learning," in Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI'22), 2022.
  23. Shenglai Zeng, ZonghangLi, HongfangYu, YihongHe, ZenglinXu, Dusit Niyato& Han Yu, “Heterogeneous Federated Learning via Grouped Sequential-to-Parallel Training,” in Proceedings of the 27th International Conference on Database Systems for Advanced Applications (DASFAA-22), 2022.
  24. Jiehuang Zhang & Han Yu, "A Methodological Framework for Facilitating Explainable AI Design," in Proceedings of the 14th International Conference on Social Computing and Social Media (SCSM’22), 2022.
  25. Yuanchao Loh, ZichenChen, YansongZhao & Han Yu, "FLAS: A Platform for Studying Attacks on Federated Learning," in Proceedings of the 14th International Conference on Social Computing and Social Media (SCSM’22), 2022. 
  26. Zelei Liu, Yuanyuan Chen, YansongZhao, Han Yu, Yang Liu, Renyi Bao, JinpengJiang, ZaiqingNie, Qian Xu & Qiang Yang, "Contribution-Aware Federated Learning for Smart Healthcare," in Proceedings of the 34th Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-22), 2022. 
  27. Daifei Feng, CiciliaHelena, Wei Yang Bryan Lim, Jer ShyuanNg, HongchaoJiang, Zehui Xiong, JiawenKang, Han Yu, Dusit Niyato & ChunyanMiao, "CrowdFL: A Marketplace for Crowdsourced Federated Learning," in Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI-22), 2022.
  28. Pye Sone Kyaw & Han Yu, "PersonalisedFederated Learning: A Combinational Approach," in Proceedings of the 1st International Student Conference on Artificial Intelligence (STCAI’21), 2021.
  29. Xianshuai Cao, YuliangShi, Han Yu, Jihu Wang, Xinjun Wang, ZhongminYan & ZhiyongChen, “DEKR: Description Enhanced Knowledge Graph for Machine Learning Method Recommendation,” in Proceedings of the 44th ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’21), 2021.
  30. Ying Shu, JiehuangZhang & Han Yu, "Fairness in Design: A Tool for Guidance in Artificial Intelligence Design," in Proceedings of the 13th International Conference on Social Computing and Social Media (SCSM’21), 2021. 
  31. Chang Liu, Han Yu, BoyangLi, Zhiqi Shen, Zhanning Gao, PeiranRen, Xuansong Xie, LizhenCui & ChunyanMiao, "Noise-resistant Deep Metric Learning with Ranking-based Instance Selection," in Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR'21), 2021. 
  32. Yuanyuan Chen, BoyangLi, Han Yu, PengchengWu & ChunyanMiao, "HyDRA: HypergradientData Relevance Analysis for Interpreting Deep Neural Networks," in Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI-21), pp. 7081–7089, 2021
  33. Marlena N. Raczkowska, SmrithiVijayakumar, Siong Peng Kwek, Arthi Thiyagarajan, V VienLee, Xavier Tadeo, Grady Ng, Ni Yin Lau, Qiao Ying Leong, Qian Yee Chai, FatinAliyah, Agata Blasiak, Wei TsauDavid Chia, Christopher Asplund, Alexandria Remus*, Dean Ho*, Balamurugan A Vellayappan, “CURATE.AI COR-Tx Trial for Post Brain Radiotherapy Patients ”, UCL Centre for BehaviourChange (CBC) Conference 2022
  34. Poster Presentation at ASCO Annual Meeting, Chicago, US (2023). Raczkowska, M. N et al. Mixed-methods clinical trial to evaluate the feasibility of the CURATE. AI optimiseddigital cognitive rehabilitation therapeutic (COR-Tx) in patients post brain radiotherapy.
  35. Oral Presentation at The Evidence and Implementation Summit, Melbourne , Australia (2023). Kwek, S.P.  Et al. Exploring the general acceptability and usability of CURATE.DTxfor implementation in a senior population“
  36. Invited Summer School Lecture at World Congress on Biosensors,  Busan, Korea (2023). Remus, A. et al. "AI in Healthcare: Applications and Opportunities to Optimise Human Potential"
  37. Oral Presentation at UCL CBC Conference: BehaviourChange for Health and Sustainability, London, UK (2022). Raczkowska, M.N et al. “CURATE.AI COR-Tx Trial for Post BrainRadiotherapyPatients”
  38. Makris, D., Agres, K., & Herremans, D. (Accepted). Sen2Seq: A Conditional seq2seq Framework for Generating Lead Sheets with Sentiment. IEEE International Joint Conference on Neural Networks. [Core A]
  39. Chua, P., Gupta, C., Agres, K., & Nanayakkara, S. (2022). Computational Music Systems for Emotional Health and Wellbeing: A Review. Proceedings of the 21st Annual Pre-ICIS Workshop on HCI Research in MIS, Denmark.
  40. Yilei Wu, … Helen Juan Zhou. Deeply supervised network for white matter hyperintensities segmentation with transfer learning, 2022, MIDL
  41. Zijiao Chen, … Helen Juan Zhou. Deep learning-based prediction of vigilance fluctuations in fMRI, 2022, OHBM
  42. Dong Z, Wu Y, Xiao Y, Chong JSX, Jin Y, Zhou JH,  Beyond the Snapshot: Brain Tokenized Graph Transformer for Longitudinal Brain Functional Connectome Embedding, The International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Vancouver, Canada, 2023.

  1. Zhang, S., Zheng, D., Tang, N., Chew, E., Lim, R. Y., Ang, K. K., & Guan, C. (2022, 4 Dec). Online Adaptive CNN: a Session-to-session Transfer Learning Approach for Non-stationary EEG. Proceedings of the IEEE Symposium on Computational Intelligence for Brain Computer Interfaces, Singapore, 164-170.

Ongoing Work

Gait Patterns Prediction from EEG

Objective is to develop a BCI to continuously predict gait patterns directly from EEG signals. A model that offers robust prediction is trained using EEG and 6 goniometers. A clinical study to perform longitudinal multimodal profiling of gait in stroke using EEG and lower limb sensors is in progress.

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