Publications

2023

  1. Yu Zhang, Wei Wu, Sharon Naparstek, Joseph Gordon, Mallissa Watts, Emmanuel Shpigel, Dawlat EI-Said, Faizan Badami, Michelle Eisenberg, Russell Toll, Allyson Gage, Madeleine Goodkind, Amit Etkin. Machine learning-based identification of a psychotherapy-predictive electroencephalographic signature in PTSD. Nature Mental Health, 2023. Accepted
  2. Kanhao Zhao, Hua Xie, Gregory A. Fonzo, Xiaoyu Tong, Nancy Carlisle, Matthieu Chidharom, Amit Etkin, Yu Zhang. Individualized fMRI connectivity defines signatures of antidepressant and placebo responses in major depression. Molecular Psychiatry. 2023. [Link]. [Code]
  3. Shuang Liang, Wenlong Hang, Baiying Lei, Jun Wang, Jin Qin, Kup-Sze Choi, Yu Zhang. Adaptive multi-model knowledge transfer matrix machine for EEG classification. IEEE Transactions on Neural Networks and Learning Systems, 2023. Accepted. [Link]
  4. Guihong Wan, Jiao Meng, Xinglong Ju, Yu Zhang, Feng Liu. Electrophysiological brain source imaging via combinatorial search with provable optimality. AAAI 2023. Accepted. (Acceptance rate < 20%)
  5. Wenlong Hang, Jiaxing Li, Shuang Liang, Yuan Wu, Baiying Lei, Jin Qin, Yu Zhang, Kup-Sze Choi. FEDEEG: Federated EEG decoding via inter-subject structure matching. ICASSP 2023. Accepted.
  6. Xuetong Wang, Kanhao Zhao, Rong Zhou, Alex Leow, Ricardo Osorio, Yu Zhang, Lifang He. Normative modeling via conditional variational autoencoder and adversarial learning to identify brain dysfunction in Alzheimer’s disease. ISBI 2023. Accepted.
  7. Caroline Ferguson, James C.M. Hwang, Yu Zhang, Xuanhong Cheng. Single-cell nuclear content classification using microwave impedance spectroscopy and machine learning. Sensors, 2023. Accepted.

2022

  1. Zhe Sage Chen, Prathamesh Kulkarni, Isaac Galatzer-Levy, Benedetta Bigio, Carla Nasca, Yu Zhang. Modern views of machine learning for precision psychiatry. Patterns (Cell Press), 2022, 3(11): 100602. [Link]
  2. Kanhao Zhao, Boris Duka, Hua Xie, Desmond J. Oathes, Vince Calhoun, Yu Zhang. A dynamic graph convolutional neural network framework reveals new insights into connectome dysfunctions in ADHD. NeuroImage, 2022, 246: 118774. [Link] [Code]
  3. Xiaoyu Tong, Hua Xie, Nancy Carlisle, Gregory A. Fonzo, Desmond J. Oathes, Jing Jiang, Yu Zhang. Transdiagnostic connectome signatures from resting-state fMRI predict individual-level intellectual capacity. Translational Psychiatry, 2022, 12: 367 [Link]
  4. Tao Zhou, Yi Zhou, Chen Gong, Jian Yang, Yu Zhang. Feature aggregation and propagation network for comouflaged object detection. IEEE Transactions on Image Processing, 2022. 31: 7036-7047. [Link]
  5. Jie Zhang, Zhe Sun, Feng Duan, Liang Shi, Yu Zhang, Jordi Solé-Casals, Cesar Caiafa. Cerebral cortex layer segmentation using diffusion MRI in vivo with application to working memory analysis. Human Brain Mapping, 2022. Accepted. [Link]
  6. Tianyi Han, Xiaoli Gong, Fan Feng, Jin Zhang, Zhe Sun, Yu Zhang. Privacy preserving multi-source domain adaptation for medical data. IEEE Journal of Biomedical and Health Informatics, 2022. Accepted. [Link]​
  7. Yu Zhang, Tao Zhou, Wei Wu, Hua Xie, Hongru Zhu, Guoxu Zhou, Andrzej Cichocki. Improving EEG decoding via clustering-based multi-task feature learning. IEEE Transactions on Neural Networks and Learning Systems, 2022, 33(8): 3587-3597. [Link]
  8. Yu Zhang, Han Zhang, Ehsan Adeli, Xiaobo Chen, Mingxia Liu, Dinggang Shen. Multiview feature learning with multiatlas based functional connectivity networks for MCI diagnosis. IEEE Transactions on Cybernetics, 2022, 52(7): 2618-2275. [Link]
  9. Chalin Yi, Ruwei Yao, Liuyi Song, Lin Jiang, Yajing Si, Peiyang Li, Fali Li, Dezhong Yao, Yu Zhang, Peng Xu. A novel method for constructing EEG large-scale cortical dynamical functional network connectivity (dFNC): WTCS. IEEE Transactions on Cybernetics, 2022, 52(12): 12869-12881. [Link]
  10. Hao Jia, Zhe Sun, Feng Duan, Yu Zhang, Cesar F. Caiafa, Jordi Sole-Casals. Improving pre-movement pattern detection with filter bank selection. Journal of Neural Engineering, 2022, 19: 066012. [Link]
  11. Yudong Pan, Jianbo Chen, Yangsong Zhang, Yu Zhang. An efficient CNN-LSTM network with spectral normalization and label smoothing technologies for SSVEP frequency recognition. Journal of Neural Engineering, 2022, 19: 056014. [Link]
  12. Zhe Liu, Yun Li, Lina Yao, Molly Lucas, Jessica Monaghan, Yu Zhang. Side-aware meta learning for cross-dataset listener diagnosis with subjective tinnitus. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022, 30: 2352-2361. [Link]
  13. Shengyuan Cai, Haoran Li, Qiang Wu, Ju Liu, Yu Zhang. Motor imagery decoding in the presence of distraction using graph sequence neural networks. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022, 30: 1716-1726 [Link]
  14. Xuesong Chen, Lina Yao, Islem Rekik, Yu Zhang. Contrastive functional connectivity graph learning for population-based fMRI Classification. MICCAI 2022. 221-230, 2022. [Link]
  15. Houliang Zhou, Yu Zhang, Brian Chen, Shen Li, Lifang He. Sparse interpretation of graph convolutional networks for multi-modal diagnosis of Alzheimer’s disease. MICCAI 2022. 469-478, 2022 [Link]​
  16. Houliang Zhou, Lifang He, Yu Zhang, Li Shen, Brian Chen. Interpretable graph convolutional network of multi-modality brain imaging for Alzheimer’s disease. ISBI 2022. [Link]
  17. Xiaocong Chen, Yun Li, Lina Yao, Ehsan Adeli, Yu Zhang, Xianzhi Wang. Generative adversarial U-net for domain-free few-shot medical diagnosis. Pattern Recognition Letters, 2022, 157: 112-118. [Link]
  18. Liang Chang, Raofen Wang, Yu Zhang. Decoding SSVEP patterns from EEG via multivariate variational mode decomposition-informed canonical correlation analysis. Biomedical Signal Processing and Control, 2022, 71: 103209. [Link]
  19. Shuang Liang, Wenlong Hang, Mingbo Yin, Hang Shen, Qiong Wang, Jin Qin, Kup-Sze Choi, Yu Zhang. Deep EEG feature learning via stacking common spatial pattern and support matrix machine. Biomedical Signal Processing and Control, 2022, 74: 103531. [Link]

2021

  1. Yu Zhang, Wei Wu, Russell Toll, Sharon Naparstek, Adi Maron-Katz, Mallissa Watts, Joseph Gordon, Jisoo Jeong, Laura Astolfi, Emmanuel Shpigel, Parker Longwell, Kamron Sarhadi, Dawlat EI-Said, Yuanqing Li, Crystal Cooper, Cherise Chin-Fatt, Martjin Arns, Madeleine Goodkind, Madhukar Trivedi, Charles Marmar, Amit Etkin. Identification of psychiatric disorder subtypes from functional connectivity patterns in resting-state electroencephalography. Nature Biomedical Engineering, 2021, 5: 309-323. [Link] Media Coverage: Forbes, Medscape, The Medical News, BioSpace, etc.​
  2. Chalin Yi, Ruwei Yao, Liuyi Song, Lin Jiang, Yajing Si, Peiyang Li, Fali Li, Dezhong Yao, Yu Zhang, Peng Xu. A novel method for constructing EEG large-scale cortical dynamical functional network connectivity (dFNC): WTCS. IEEE Transactions on Cybernetics, 2021, Accepted. [Link]
  3. Xiang Zhang, Lina Yao, Xianzhi Wang, Jessica Monaghan, David Mcalpine, Yu Zhang. A survey on deep learning based non-invasive brain signals: recent advances and new frontiers. Journal of Neural Engineering, 2021, 18: 031002. [Link]
  4. Ke Qin, Raofen Wang, Yu Zhang. Filter bank-driven multivariate synchronization index for training-free SSVEP BCI. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2021, 29: 934-943. [Link]
  5. Hao Jia, Feng Duan, Zhe Sun, Kai Zhang, Yangyang Dai, Yu Zhang. Decoding premovement patterns with task related component analysis. Cognitive Computation, 2021, 13: 1389-1405. [Link]
  6. Xiaocong Chen, Lina Yao, Tao Zhou, Jinming Dong, Yu Zhang. Momentum contrastive learning for few-shot COVID-19 diagnosis from chest CT images. Pattern Recognition, 2021, 113: 107826. [Link]
  7. Yangyang Dai, Feng Duan, Fan Feng, Zhe Sun, Yu Zhang, Cesar Caiafa, Pere Marti-Puig, Jordi Sole-Casals. A fast approach to removing muscle artifacts for EEG with signal serialization based ensemble empirical mode decomposition. Entropy, 2021, 23(9): 1170. [Link]​​​
  8. Xiaocong Chen, Lina Yao, Yu Zhang. Residual attention U-net for automated multi-class segmentation of COVID-19 chest CT images. ISMB/ECCB 2021, 2021. [Link]​​

2020

  1. Wei Wu, Yu Zhang, Jing Jiang, Molly Lucas, Gregory Fonzo, Camarin Rolle, Crystal Cooper, Cherise Chin-Fatt, Noralie Krepel, Carena Cornelssen, Rachael Wright, Russell Toll, Hersh Trivedi, Karen Monuszko, Trevor Caudle, Kamron Sarhadi, Joseph Trombello, Thilo Deckersbach, Phil Adams, Myrna Weissman, Maurizio Fava, Diego Pizzagalli, Martijn Arns, Madhukar Trivedi, Amit Etkin. An electroencephalographic signature predicts antidepressant response in major depression. Nature Biotechnology, 2020, 38(4): 439-447. [Link]
  2. Russell Toll, Wei Wu, Sharon Naparstek, Yu Zhang, Manjari Narayan, Brian Patenaude, Carlo Angeles, Kasra Sarhadi, Nicole Anicetti, Emmanuel Shpigel, Rachael Wright, Jennifer Newman, Bryan Gonzalez, Roland Hart, Silas Mann, Duna Abu-Amara, Kamron Sarhadi, Carena Cornelssen, Charles Marmar, Amit Etkin. An electroencephalography connectomic profile of post-traumatic stress disorder. American Journal of Psychiatry, 2020, 177(3): 233-243. [Link]​
  3. Adi Maron-Katz, Yu Zhang, Manjari Narayan, Wei Wu, Russell Toll, Sharon Naparstek, Carlo Angeles, Parker Longwell, Emmanuel Shpigel, Jennifer Newman, Duna Amara, Charles Marmar, Amit Etkin. Individual patterns of abnormality in resting-state functional connectivity reveal two data-driven PTSD subgroups. American Journal of Psychiatry, 2020, 177(3): 244-253. [Link]​
  4. Yong Jiao, Tao Zhou, Lina Yao, Guoxu Zhou, Xingyu Wang, Yu Zhang. Multi-view multi-scale optimization of feature representation for EEG classification improvement. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2020, 28(12): 2589-2597. [Link]
  5. Zhen Zhou, Xiaobo Chen, Yu Zhang, Lishan Qiao, Renping Yu, Gang Pan, Han Zhang, Dinggang Shen. A toolbox for brain network construction and classification (BrainNetClass). Human Brain Mapping, 2020, 41: 2808-2826. [Link] [Toolbox Code in Matlab]​
  6. Xiang Zhang, Lina Yao, Manqing Dong, Zhe Liu, Yu Zhang, Yong Li. Adversarial representation learning for robust patient-independent eplileptic seizure detection. IEEE Journal of Biomedical and Health Informatics, 2020, 24(10): 2852-2859. [Link]
  7. Zhichao Jin, Guoxu Zhou, Daqi Gao, Yu Zhang. EEG classification using sparse Bayesian extreme learning machine for brain-computer interface. Neural Computing and Applications, 2020, 32: 6601-6609. [Link]

2019

  1. Gregory Fonzo#, Amit Etkin#, Yu Zhang#, Wei Wu, Crystal Cooper, Cherise Chin-Fatt, Manish K. Jha, Joseph Trombello, Thilo Deckersbach, Phil Adams, Melvin McInnis, Patrick J. McGrath, Myrna M. Weissman, Maurizio Fava, Madhukar H. Trivedi. Brain regulation of emotional conflict predicts antidepressant treatment response for depression. Nature Human Behaviour, 2019, 13: 1319-1331. [Link] (#Co-first authors)
  2. Yangsong Zhang, Erwei Yin, Fali Li, Yu Zhang, Daqing Guo, Dezhong Yao, Peng Xu. Hierarchical feature fusion framework for frequency recognition in SSVEP-based BCIs. Neural Networks, 2019, 199: 1-9. [Link]
  3. Xiang Zhang, Lina Yao, Xianzhi Wang, Jessica Monaghan, David Mcalpine, Yu Zhang. A survey on deep learning based brain computer interface: Recent advances and new frontiers. arXiv, 2019, 1-66. [Link]
  4. Yu Zhang, Chang S. Nam, Guoxu Zhou, Jing Jin, Xingyu Wang, Andrzej Cichocki. Temporally constrained sparse group spatial patterns for motor imagery BCI. IEEE Transactions on Cybernetics, 2019, 49(9): 3322-3332. [Link]​
  5. Yu Zhang, Han Zhang, Xiaobo Chen, Mingxia Liu, Xiaofeng Zhu, Seong-Whan Lee, Dinggang Shen. Strength and similarity guided group-level brain functional network construction for MCI diagnosis. Pattern Recognition, 2019, 88: 421-430. [Link] [Matlab Code]
  6. Qiang Wu, Yu Zhang, Ju Liu, Jiande Sun, Andrzej Cichocki. Regularized group sparse discriminant analysis for P300-based brain computer interface. International Journal of Neural Systems, 2019, 6: 1950002. [Link]
  7. Yong Jiao, Yu Zhang, Xun Chen, Erwei Yin, Jing Jin, Xingyu Wang, Andrzej Cichocki. Sparse group representation model for motor imagery EEG classification. IEEE Journal of Biomedical and Health Informatics, 2019, 23(2): 631-641. [Link]
  8. Tao Zhou, K.M. Thung, Yu Zhang, Dinggang Shen, Inter-modality dependence induced data recovery for MCI conversion prediction, In: 22th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2019), 186-195. [Link]
  9. Yuning Qiu, Guoxu Zhou, Yu Zhang, Shengli Xie. Graph regularized nonnegative tucker decomposition for tensor data representation. In: 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2019), 8613-8617. [Link]

2018​

  1. Yangsong Zhang, Erwei Yin, Fali Li, Yu Zhang, Toshihisa Tanaka, Qibin Zhao, Yan Cui, Peng Xu, Dezhong Yao, and Daqing Guo. Two-stage frequency recognition method based on correlated component analysis for SSVEP-based BCI. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2018, 26(7): 1314-1323. [Link]
  2. Yu Zhang, Yu Wang, Guoxu Zhou, Jing Jin, Bei Wang, Xingyu Wang, Andrzej Cichocki. Multi-kernel extreme learning machine for EEG classification in brain-computer interfaces. Expert Systems with Applications, 2018, 96: 302-310. [Link]​
  3. Yong Jiao, Yu Zhang, Yu Wang, Bei Wang, Jing Jin, Xingyu Wang. A novel multilayer correlation maximization model for improving CCA-based frequency recognition in SSVEP brain-computer interface. International Journal of Neural Systems, 2018, 28(4): 1750039 (14 pages). [PDF] [Link]
  4. Xun Chen, Qiang Chen, Yu Zhang, Z. Jane Wang. A novel EEMD-CCA approach to removing muscle artifacts for pervasive EEG. IEEE Sensors Journal, 2018, 19(19): 8420-8431. [Link]
  5. Yangsong Zhang, Daqing Guo, Erwei Yin, Fali Li, Peiyang Li, Yu Zhang, Qibin Zhao, Toshihisa Tanaka, Dezhong Yao, Peng Xu. Correlated component analysis for enhancing the performance of SSVEP-based brain-computer interface. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2018, 26(5): 948-956. [Link]
  6. Xueyuan Xu, Xun Chen, Yu Zhang. Removal of muscle artifacts from few-channel EEG recordings based on multivariate empirical model decomposition and independent vector analysis. Electronics Letters, 2018, 54(14): 866-868. [Link]
  7. Na Liu, Lihong Wan, Yu Zhang, Tao Zhou, Hong Huo, Tao Fang. Exploiting convolutional neural networks with deeply local description for remote sensing image classification. IEEE Access, 2018, 6(1): 11215-11228. [Link]
  8. Minqiang Huang, Jing Jin, Yu Zhang, Dewen Hu, Xingyu Wang. Usage of drip drops as stimuli in an auditory P300 BCI paradigm. Cognitive Neurodynamics, 2018, 12(1): 85-94. [Link]
  9. Jiao Cheng, Jing Jin, Ian Daly, Yu Zhang, Bei Wang, Xingyu Wang, and Andrzej Cichocki. Effect of a combination of flip and zooming stimuli on the performance of a visual brain-computer interface for spelling. Biomedical Engineering/Biomedizinische Technik, 2018, 64(1): 29-38.

2017

  1. Yu Zhang, Yu Wang, Jing Jin, Xingyu Wang. Sparse Bayesian Learning for obtaining sparsity of EEG frequency bands based feature vectors in Motor Imagery Classification. International Journal of Neural Systems, vol. 27, no. 2, p. 1650032 (13 pages), 2017. [PDF] [Link]
  2. Yu Zhang, Guoxu Zhou, Jing Jin, Yangsong Zhang, Xingyu Wang, Andrzej Cichocki. Sparse Bayesian multiway canonical correlation analysis for EEG pattern recognition. Neurocomputing, vol. 225, pp. 103-110, 2017. [PDF] [Link]
  3. Yu Zhang, Han Zhang, Xiaobo Chen, Seong-Whan Lee, Dinggang Shen. Hybrid high-order functional connectivity networks using resting-state functional MRI for mild cognitive impairment diagnosis​. Scientific Reports, vol. 7, article 6530, 2017. [PDF] [Link]
  4. Han Zhang, Xiaobo Chen, Yu Zhang, Dinggang Shen. Test-retest reliability of high-order functional connectivity in young healthy adults. Frontiers in Neuroscience, 2017, 11: Article 439, 1-20. [PDF] [Link]
  5. Zhaoyang Qiu, Brendan Allison, Jing Jin, Yu Zhang, Xingyu Wang, Wei Li, Andrzej Cichocki. Optimized motor imagery paradigm based on imagining Chinese characters writing movement. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2017, 25(7): 1009-1017. [Link] 
  6. Dong Qian, Bei Wang, Xiangyun Qing, Tao Zhang, Yu Zhang, Xingyu Wang, Masatoshi Nakamura. Drowsiness detection by Bayesian-Copula discriminant classifier Based on EEG signals during daytime short nap. IEEE Transactions on Biomedical Engineering, vol. 64, no. 4, pp. 743-754, 2017. [Link]
  7. Dong Qian, Bei Wang, Xiangyun Qing, Tao Zhang, Yu Zhang, Xingyu Wang, Masatoshi Nakamura. Bayesian nonnegative CP decomposition-based feature extraction algorithm for drowsiness detection. IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 25, no. 8, pp. 1297-1308, 2017. [Link]

2016

  1. Yu Zhang, Guoxu Zhou, Jing Jin, Qibin Zhao, Xingyu Wang, Andrzej Cichocki. Sparse Bayesian classification of EEG for brain-computer interface. IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 11, pp. 2256-2267, 2016. [PDF] [Link]
  2. Yu Zhang, Guoxu Zhou, Qibin Zhao, Xingyu Wang, Andrzej Cichocki, Fast nonnegative tensor factorization based on accelerated proximal gradient and low-rank approximation, Neurocomputing, vol. 198, pp. 148-154, 2016. [PDF] [Link] [Matlab Code]
  3. Haiqiang Wang, Yu Zhang, Nicholas Waytowich, Dean Krusienski, Guoxu Zhou, Jing Jin, Xingyu Wang, Andrzej Cichocki. Discriminative feature extraction via multivariate linear regression for SSVEP-based BCI. IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 24, no. 5, pp. 532-541, 2016. [PDF] [Link] [Matlab Code]
  4. Guoxu Zhou, Andrzej Cichocki, Yu Zhang, Danilo Mandic. Group component analysis for multiblock data: common and individual feature extraction. IEEE Transactions on Neural Networks and Learning Systems, 2016, vol. 27, no.11, pp. 2426-2439. [PDF] [Link] [Matlab Code]
  5. Guoxu Zhou, Qibin Zhao, Yu Zhang, Tulay Adali, Shengli Xie, Andrzej Cichocki. Linked component analysis from matrices to high order tensors: Applications to biomedical data. Proceedings of the IEEE, vol. 104, no. 2, pp. 310-331, 2016. [PDF] [Link] [Matlab Code]
  6. Long Chen, Jing Jin, Ian Daly, Yu Zhang, Xingyu Wang, and Andrzej Cichocki. Exploring combinations of different color and facial expression stimuli for gaze-independent BCIs. Frontiers in Computational Neuroscience, vol. 10, pp. Article 5, 2016. [PDF] [Link]
  7. Zhaoyang Qiu, Jing Jin, Hak-Keung Lam, Yu Zhang, Xingyu Wang, Andrzej Cichocki. Improved SFFS method for channel selection in motor imagery based BCI. Neurocomputing, vol. 207, pp. 519-527, 2016. [Link]
  8. Yangsong Zhang, Daqing Guo, Peng Xu, Yu Zhang, Dezhong Yao. Robust frequency recognition for SSVEP-based BCI with temporally local multivariate synchronization index. Cognitive Neurodynamics, vol. 10, no.6, pp. 505-511, 2016. [Link]
  9. Minqiang Huang, Ian Daly, Jing Jin, Yu Zhang, Xingyu Wang, and Andrzej Cichocki. An exploration of spatial auditory BCI paradigms with different sounds: music notes versus beeps. Cognitive Neurodynamics, vol. 10, no. 3, pp. 201-209, 2016. [PDF] [Link]

2015​

  1. Yu Zhang, Guoxu Zhou, Jing Jin, Xingyu Wang, Andrzej Cichocki. Optimizing spatial patterns with sparse filter bands for motor-imagery based brain-computer interface. Journal of Neuroscience Methods, vol. 255, pp. 85-91, 2015. [PDF] [Link]
  2. Yu Zhang, Guoxu Zhou, Jing Jin, Xingyu Wang, Andrzej Cichocki. SSVEP recognition using common feature analysis in brain-computer interface. Journal of Neuroscience Methods, vol. 244, pp. 8-15, 2015. [PDF] [Link]
  3. Jing Jin, Eric Selles, Sijie Zhou, Yu Zhang, Xingyu Wang, Andrzej Cichocki. A P300 brain computer interface based on a modification of the mismatch negativity paradigm. International Journal of Neural Systems, vol. 25, no. 3, pp. 1550011, 2015. [PDF] [Link]
  4. Raofen Wang, Yu Zhang, Liping Zhang. An adaptive neural network approach for operator function state prediction using phychophysiological data. Integrated Computer-Aided Engineering, vol. 23, no. 1, pp. 81-97, 2015. [Link]
  5. Jiaxin Ma, Yu Zhang, Andrzej Cichocki, Fumitoshi Mastuno. A novel EOG/EEG hybrid human-machine interface adopting eye movements and ERP: application to robot control. IEEE Transactions on Biomedical Engineering, vol. 62, no. 3, pp. 876-889, 2015. [PDF] [Link]
  6. Long Chen, Jing Jin, Yu Zhang, Xingyu Wang, Andrzej Cichocki. A survey of the dummy face and human face stimuli used in BCI paradigm. Journal of Neuroscience Methods, vol. 239, pp. 18-27, 2015. [PDF] [Link]
  7. Minjue Wang, Ian Daly, Brendan Allison, Jing Jin, Yu Zhang, Long Chen, Xingyu Wang. A new hybrid BCI paradigm based on P300 and SSVEP. Journal of Neuroscience Methods, vol. 244, pp.16-25, 2015. [PDF] [Link]

2014

  1. Yu Zhang, Guoxu Zhou, Jing Jin, Xingyu Wang, Andrzej Cichocki. Frequency recognition in SSVEP-based BCI using multiset canonical correlation analysis. International Journal of Neural Systems, vol. 24, no. 4, pp. 1450013 (14 pages), 2014. [PDF] [Link] [Matlab Code]
  2. Yu Zhang, Guoxu Zhou, Jing Jin, Qibin Zhao, Xingyu Wang, Andrzej Cichocki. Aggregation of sparse linear discriminant analysis for event-related potential classification in brain-computer interface. International Journal of Neural Systems, vol.24, no.1, pp. 1450003 (15 pages), 2014. [PDF] [Link]
  3. Jing Jin, Brendan Allison, Yu Zhang, Xingyu Wang, Andrzej Cichocki. An ERP-based BCI using an oddball paradigm with different faces and reduced errors in critical function. International Journal of Neural Systems, vol. 24, no. 8, pp. 1450027, 2014. [Link]
  4. Jing Jin, Ian Daly, Yu Zhang, Xingyu Wang, Andrzej Cichocki. An optimized ERP brain-computer interface based on facial expression changes. Journal of Neural Engineering, vol. 11, no. 3, pp. 036004, 2014. [PDF] [Link]
  5. Brendan Allison, Jing Jin, Yu Zhang, Xingyu Wang. A four-choice hybrid P300/SSVEP BCI for improved accuracy, Brain-Computer Interfaces, vol. 1, no. 1, pp. 17-26, 2014. [Link]
  6. Yangsong Zhang, Li Dong, Rui Zhang, Dezhong Yao, Yu Zhang, Peng Xu. An Efficient Frequency Recognition Method based on Likelihood Ratio Test for SSVEP-based BCI. Computational and Mathematical Methods in Medicine, vol. 2014, Article ID 908719, 7 pages, 2014. [Link]

2013

  1. Yu Zhang, Guoxu Zhou, Jing Jin, Minjue Wang, Xingyu Wang, Andrzej Cichocki. L1-regularized multiway canonical correlation analysis for SSVEP-based BCI.  IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 21, no.6, pp. 887-896, 2013. (Featured Article) [PDF] [Link] [Matlab Code]
  2. Yu Zhang, Guoxu Zhou, Qibin Zhao, Jing Jin, Xingyu Wang, Andrzej Cichocki. Spatial-temporal discriminant analysis for ERP-based brain-computer interface. IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 21, no. 2, pp. 233-243, 2013. [PDF] [Link] [Matlab Code]
  3. Jing Jin, Eric Sellers, Yu Zhang, Ian Daly, Xingyu Wang, Andrzej Cichocki. Whether generic model works for rapid ERP-based BCI calibration. Journal of Neuroscience Methods, vol. 212, no. 1, pp. 94-99, 2013. [Link]
  4. Xingyu Wang, Jing Jin, Yu Zhang, Bei Wang. Brain control: Human-computer integration control based on brain-computer interface. Acta Automatica Sinica, vol. 39, no. 3, pp. 208-211, 2013. [Link]

2012

  1. Yu Zhang, Qibin Zhao, Jing Jin, Xingyu Wang, Andrzej Cichocki. A novel BCI based on ERP components sensitive to configural processing of human faces. Journal of Neural Engineering, vol. 9, no. 2, pp. 026018, 2012. [PDF] [Link]
  2. Yu Zhang, Jing Jin, Xiangyun Qing, Bei Wang, Xingyu Wang. LASSO based stimulus frequency recognition model for SSVEP BCIs. Biomedical Signal Processing and Control, vol. 7, no. 4, pp. 104-111, 2012. (Top 25 Hottest Articles in 2012) [PDF] [Link] [Matlab Code]
  3. Jing Jin, Brendan Allison, Tobias Kaufmann, Andrea Kübler, Yu Zhang, Xingyu Wang, Andrzej Cichocki. The changing face of P300 BCIs: A comparison of stimulus changes in a P300 BCI involving faces, emotion, and movement. PLoS One, vol. 7, no. 11, pp. e49688, 2012. [Link]
  4. Raofen Wang, Jianhua Zhang, Yu Zhang, Xingyu Wang. Assessment of human operator functional state using a novel differential evolution optimization based adaptive fuzzy model. Biomedical Signal Processing and Control, vol. 7, no.5, pp. 490-498, 2012. [Link]​