Publications

In Progress

  1. Xiaoyu Tong, Hua Xie, Wei Wu, Corey J. Keller, Gregory A. Fonzo, Matthieu Chidharom, Nancy Carlisle, Amit Etkin, Yu ZhangIndividual deviations from normative electroencephalographic connectivity predict antidepressant response. (Submitted)
  2. Zesheng Ye, Lina Yao, Yu Zhang, Silvia Gustin. See what you see: Self-supervised cross-modal retrieval of visual stimuli from brain activity. (Submitted)
  3. Yong Jiao, Guoxu Zhou, Lifang He, Lina Yao, Yu ZhangA functional system-informed graph neural network framework to quantify interpretable brain dysfunction in ASD. (Submitted)
  4. Sharon Naparstek, Manjari Narayan, Mallissa Watts, Yu Zhang, Adi Maron-Katz, Faizan Badami, Joe Gordon, Michelle L. Eisenberg, Ruth O’Hara, Amit Etkin. Verbal memory abilities and EEG resting state connectivity predict psychotherapy outcome in veterans with PTSD. (Submitted)
  5. Xia Yang, Hongru Zhu, …, Jeremy Cold, Andrew J. GreenshawTao Li, Yu Zhang, Wanjun Guo. Multivariate classification based on large-scale brain networks during early abstinence predicted relapse among male detoxified alcohol-dependent patients. (Submitted) [Link]
  6. Haowei Lou, Ye Zesheng, Lina Yao, Yu ZhangLess is more: Brain functional connectivity empowered generalisable intention classification with task-relevant channel selection. (Submitted)​​​
  7. Hongru Zhu, Hua Xie, Yu Zhang, Minlan Yuan, Yuchen Li, Wei Dong, Xiaoqi Huang, Su Lui, Qiyong Gong, Changjian Qiu, Wei Zhang. A connectome-wide functional signature of visual working memory for earthquake survivors. (Submitted)​​
  8. Hao Jia, Zhe Sun, Feng Duan, Yu Zhang, Jordi Sole-Casals, Cesar Caiafa. Towards multi-class pre-movement classification. (Submitted)
  9. Tao Zhou, Huazhu Fu, Yi Zhou, Geng Chen, Yu Zhang, Chen Gong, Ling Shao. Multi-modal MR image synthesis via cross-mutual siamese networks. (Submitted)
  10. Xiaobo Chen, Yuxiang Gao, Feng Zhao, Qiaolin Ye, Jian Yang, Yu ZhangMulti-view classification via twin projection vector machine with application to EEG-based driving fatigue detection. (Submitted)
  11. Hao Jia, Zihao Huang, Cesar F. Caiafa,  Feng Duan, Yu Zhang, Zhenglu Yang, Andrzej Cichocki, Zhe Sun, Jordi Sole-Casals. Data augmentation in EEG functional connectivity matrices. (Submitted)
  12. Wenlong Hang, Zengguang Li, Shuang Liang, Yuanpeng Zhang, Baiying Lei, Jing Qin, Yu Zhang, Kup-Sze Choi. Structure invariance-driven collaborative contrastive network for EEG recognition. (Submitted)
  13. Hao Jia, Fan Feng, Cesar F. Caiafa, Feng Duan, Yu Zhang, Zhe Sun, Jordi Sole-Casals. Towards a Multi-class Classification of Upper Limb Movements. (Submitted)
  14. Hao Jia, Cesar F. Caiafa, Feng Duan, Yu Zhang, Zhe Sun, Jordi Sole-Casals. Enabling temporal-spectral decoding in pre-movement decoding. (In preparation)
  15. Xuesong Wang, Kanhao Zhao, Lina Yao, Yu ZhangSubtype identification of neurological disorders via clustering on label-guided contrastive functional connectivity graph. (In preparation)
  16. Houliang Zhou, Lifang He, Brian Chen, Li Shen, Yu ZhangMultimodal diagnosis of Alzheimer’s disease using interpretable graph convolutional networks. (In preparation)

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 ZhangIndividualized fMRI connectivity defines signatures of antidepressant and placebo responses in major depressionMolecular Psychiatry. 2023. [Link]. [Code]
  3. Shuang Liang, Wenlong Hang, Baiying Lei, Jun Wang, Jin Qin, Kup-Sze Choi, Yu ZhangAdaptive multi-model knowledge transfer matrix machine for EEG classificationIEEE 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 optimalityAAAI 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 matchingICASSP 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 diseaseISBI 2023. Accepted.
  7. Caroline Ferguson, James C.M. Hwang, Yu Zhang, Xuanhong Cheng. Single-cell nuclear content classification using microwave impedance spectroscopy and machine learningSensors, 2023. Accepted.

2022

  1. Zhe Sage Chen, Prathamesh Kulkarni, Isaac Galatzer-Levy, Benedetta Bigio, Carla Nasca, Yu ZhangModern views of machine learning for precision psychiatryPatterns (Cell Press), 2022, 3(11): 100602. [Link]
  2. Kanhao Zhao, Boris Duka, Hua Xie, Desmond J. Oathes, Vince Calhoun, Yu ZhangA dynamic graph convolutional neural network framework reveals new insights into connectome dysfunctions in ADHDNeuroImage, 2022, 246: 118774. [Link] [Code]
  3. Xiaoyu Tong, Hua Xie, Nancy Carlisle, Gregory A. Fonzo, Desmond J. Oathes, Jing Jiang, Yu ZhangTransdiagnostic connectome signatures from resting-state fMRI predict individual-level intellectual capacityTranslational Psychiatry, 2022, 12: 367 [Link]
  4. Tao Zhou, Yi Zhou, Chen Gong, Jian Yang, Yu ZhangFeature aggregation and propagation network for comouflaged object detectionIEEE 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 analysisHuman Brain Mapping, 2022. Accepted. [Link]
  6. Tianyi Han, Xiaoli Gong, Fan Feng, Jin Zhang, Zhe Sun, Yu ZhangPrivacy preserving multi-source domain adaptation for medical dataIEEE 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 learningIEEE 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 diagnosisIEEE 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): WTCSIEEE 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 selectionJournal of Neural Engineering, 2022, 19: 066012. [Link]
  11. Yudong Pan, Jianbo Chen, Yangsong Zhang, Yu ZhangAn efficient CNN-LSTM network with spectral normalization and label smoothing technologies for SSVEP frequency recognitionJournal of Neural Engineering, 2022, 19: 056014. [Link]
  12. Zhe Liu, Yun Li, Lina Yao, Molly Lucas, Jessica Monaghan, Yu ZhangSide-aware meta learning for cross-dataset listener diagnosis with subjective tinnitusIEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022, 30: 2352-2361. [Link]
  13. Shengyuan Cai, Haoran Li, Qiang Wu, Ju Liu, Yu ZhangMotor imagery decoding in the presence of distraction using graph sequence neural networksIEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022, 30: 1716-1726 [Link]
  14. Xuesong Chen, Lina Yao, Islem Rekik, Yu ZhangContrastive functional connectivity graph learning for population-based fMRI ClassificationMICCAI 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 diseaseMICCAI 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 diseaseISBI 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 diagnosisPattern Recognition Letters, 2022, 157: 112-118. [Link]
  18. Liang Chang, Raofen Wang, Yu ZhangDecoding SSVEP patterns from EEG via multivariate variational mode decomposition-informed canonical correlation analysisBiomedical 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 ZhangDeep EEG feature learning via stacking common spatial pattern and support matrix machineBiomedical 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 electroencephalographyNature Biomedical Engineering, 2021, 5: 309-323. [LinkMedia Coverage: ForbesMedscapeThe Medical NewsBioSpace, 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): WTCSIEEE Transactions on Cybernetics, 2021, Accepted. [Link]
  3. Xiang Zhang, Lina Yao, Xianzhi Wang, Jessica Monaghan, David Mcalpine, Yu ZhangA survey on deep learning based non-invasive brain signals: recent advances and new frontiersJournal of Neural Engineering, 2021, 18: 031002. [Link]
  4. Ke Qin, Raofen Wang, Yu ZhangFilter bank-driven multivariate synchronization index for training-free SSVEP BCIIEEE Transactions on Neural Systems and Rehabilitation Engineering, 2021, 29: 934-943. [Link]
  5. Hao Jia, Feng Duan, Zhe Sun, Kai Zhang, Yangyang Dai, Yu ZhangDecoding premovement patterns with task related component analysisCognitive Computation, 2021, 13: 1389-1405. [Link]
  6. Xiaocong Chen, Lina Yao, Tao Zhou, Jinming Dong, Yu ZhangMomentum contrastive learning for few-shot COVID-19 diagnosis from chest CT imagesPattern 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 decompositionEntropy, 2021, 23(9): 1170. [Link]
  8. Xiaocong Chen, Lina Yao, Yu ZhangResidual attention U-net for automated multi-class segmentation of COVID-19 chest CT imagesISMB/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 depressionNature 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 disorderAmerican 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 subgroupsAmerican Journal of Psychiatry, 2020, 177(3): 244-253. [Link]
  4. Yong Jiao, Tao Zhou, Lina Yao, Guoxu Zhou, Xingyu Wang, Yu ZhangMulti-view multi-scale optimization of feature representation for EEG classification improvementIEEE 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 detectionIEEE Journal of Biomedical and Health Informatics, 2020, 24(10): 2852-2859. [Link]
  7. Zhichao Jin, Guoxu Zhou, Daqi Gao, Yu ZhangEEG classification using sparse Bayesian extreme learning machine for brain-computer interfaceNeural 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 depressionNature 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 BCIsNeural Networks, 2019, 199: 1-9. [Link]
  3. Xiang Zhang, Lina Yao, Xianzhi Wang, Jessica Monaghan, David Mcalpine, Yu ZhangA survey on deep learning based brain computer interface: Recent advances and new frontiersarXiv, 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 BCIIEEE 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 diagnosisPattern 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 interfaceInternational 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 classificationIEEE 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 predictionIn: 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 representationIn: 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 BCIIEEE 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 interfacesExpert 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 interfaceInternational 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 EEGIEEE 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 interfaceIEEE Transactions on Neural Systems and Rehabilitation Engineering, 2018, 26(5): 948-956. [Link]
  6. Jia, Xiuyi, Weiwei Li, Junyu Liu, Yu ZhangLabel Distribution Learning by Exploiting Label CorrelationsIn: 32th AAAI Conference on Artificial Intelligence (AAAI 2018), 1-8, 2018. [Link]
  7. Xueyuan Xu, Xun Chen, Yu ZhangRemoval of muscle artifacts from few-channel EEG recordings based on multivariate empirical model decomposition and independent vector analysisElectronics Letters, 2018, 54(14): 866-868. [Link]
  8. Na Liu, Lihong Wan, Yu Zhang, Tao Zhou, Hong Huo, Tao Fang. Exploiting convolutional neural networks with deeply local description for remote sensing image classificationIEEE Access, 2018, 6(1): 11215-11228. [Link]
  9. Minqiang Huang, Jing Jin, Yu Zhang, Dewen Hu, Xingyu Wang. Usage of drip drops as stimuli in an auditory P300 BCI paradigmCognitive Neurodynamics, 2018, 12(1): 85-94. [Link]
  10. 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 spellingBiomedical 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 ClassificationInternational 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 recognitionNeurocomputing, 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 movementIEEE 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 detectionIEEE 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 interfaceIEEE 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 approximationNeurocomputing, 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 BCIIEEE 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 extractionIEEE 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 BCIsFrontiers 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 BCINeurocomputing, 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 indexCognitive 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 beepsCognitive 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 interfaceJournal 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 paradigmInternational 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 dataIntegrated 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 controlIEEE 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 paradigmJournal 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 SSVEPJournal 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 analysisInternational 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 interfaceInternational 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 functionInternational 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 changesJournal 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 accuracyBrain-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 BCIComputational 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 interfaceIEEE 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 calibrationJournal 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 interfaceActa 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 facesJournal 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 BCIsBiomedical 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 movementPLoS 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 modelBiomedical Signal Processing and Control, vol. 7, no.5, pp. 490-498, 2012. [Link]​

 

​​Selected Conference Publications

  1. Jia, Xiuyi, Weiwei Li, Junyu Liu, Yu ZhangLabel Distribution Learning by Exploiting Label CorrelationsIn: 32th AAAI Conference on Artificial Intelligence (AAAI 2018), 1-8, 2018.
  2. X. Chen, H. Zhang, Y. Zhang, J. Yang, D. Shen. Learning pairwise similarity guided sparse functional connectivity network for MCI classificationIn: 4th Asian Conference on Pattern Recognition (ACPR 2017), Accepted. (Best Paper Award)
  3. Y. Zhang, H. Zhang, X. Chen, M. Liu, X. Zhu, D. Shen. Inter-subject similarity guided brain network modeling for MCI diagnosisIn: 8th International MICCAI Workshop on Machine Learning in Medical Imaging (MLMI 2017), 168-175.
  4. Y. Zhang, H. Zhang, X. Chen, D. Shen. Constructing multi-frequency high-order functional connectivity network for diagnosis of mild cognitive impairmentIn: 1st International MICCAI Workshop on Connectomics in NeuroImaging (CNI 2017), 9-16.
  5. X. Zhu, K.M. Thung, E. Adeli, Y. Zhang, D. Shen. Maximum mean discrepancy based multiple kernel learning for incomplete multimodality neuroimaging dataIn: 20th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2017), Accepted.
  6. Y. Zhang, Q. Zhao, G. Zhou, J. Jin, X. Wang, A. Cichocki. Removal of EEG artifacts for BCI applications using fully Bayesian tensor completionIn: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2016), pp. 819-823, 2016. [Link]
  7. Y. Zhang, J. Jin, X. Wang, Y. Wang. Motor imagery EEG classification via Bayesian extreme learning machineIn: 6th IEEE International Conference on Information Science and Technology (ICIST 2016)pp. 27-30, 2016. [Link]
  8. Y. Zhang, Y. Wang, J. Jin, X. Wang. Sparse Support Vector Machine for Simultaneous Feature Selection and Classification in Motor-Imagery-Based BCIIn: the 5th International Conference on Cognitive Neurodynamics (ICCN 2015)Springer, pp. 363-369, 2016. [Link​]
  9. Yong Jiao, Yu Zhang, Jing Jin, Xingyu Wang. Multilayer correlation maximization for frequency recognition in SSVEP brain-computer interfaceIn: 6th IEEE International Conference on Information Science and Technology (ICIST 2016), pp. 31-35, 2016. [Link]
  10. Zhaoyang Qiu, Jing Jin, Yu Zhang, Xingyu Wang. Generic Channels Selection in Motor Imagery-Based BCI. In: 5th IEEE International Conference on Cognitive Neurodynamics (ICCN 2015), Springer, pp. 413-419, January 2016. [Link]
  11. Haiqiang Wang, Yu Zhang, Jing Jin, Xingyu Wang. SSVEP recognition using multivariate linear regression for brain computer interfaceIn: 2015 IEEE International Conference on Computer and Communications (ICCC 2015), pp. 176-180, Chengdu, China, Oct. 2015. [Link]
  12. Hanhan Zhang, Jing Jin, Sijie Zhou, Yu Zhang, Xingyu Wang. Improving the performance of online classifier by removing the error samples from offline training dataIn: 2015 IEEE International Conference on Computer and Communications (ICCC 2015), pp. 77-81, 2015. [Link]
  13. Huiping Hu, Jing Jin, Yu Zhang, Xingyu Wang. Design of a visual information-based brain-computer interface control systemIn: 11th IEEE International Conference on Natural Computation (ICNC 2015), 2015, pp. 887-891. [Link]
  14. J. Jin, Y. Zhang, X. Wang, I. Daly, A. Cichocki. Decreasing the interference of visual-based P300 BCI using facial expression changesIn: IEEE 11th World Congress on Intelligent Control and Automation (WCICA 2014), pp. 2407-2411, Shenyang, China, 29 June 2014. [Link]
  15. G. Zhou, Q. Zhao, Y. Zhang, A. Cichocki. Fast Nonnegative Tensor Factorization by Using Accelerated Proximal GradientIn: 11th International Symposium on Neural Networks (ISNN 2014), pp. 459-468. Springer, Hongkong and Macao, China, 28 November 2014. [Link]
  16. Y. Zhang, H. Ma, J. Jin, X. Wang. Adaptive strategy for time window length in SSVEP-based brain-computer interfaceIn: 2014 IEEE International Conference on Mechatronics and Control (ICMC 2014), pp. 140-143, Jinzhou, China, 3-5 July 2014. [Link]
  17. J. Ma, Y. Zhang, Y. Nam, A. Cichocki, F. Matsuno. EOG/ERP hybrid human-machine interface for robot control. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2013), pp. 859-864, Tokyo, Japan, 3-7 November 2013. [Link]
  18. Y. Zhang, J. Jin, B. Wang, X. Wang. Shrinkage common spatial pattern for feature extraction in brain-computer interfaceIn: 2013 Chinese Intelligent Automation Conference (CIAC 2013), Lecture Notes in Electrical Engineering (LNEE), Springer, pp. 155-161, Yangzhou, China, 23-25 August 2013. [PDF] [Link]
  19. Q. Zhao, Y. Zhang, A. Onishi, A. Cichocki. An effective BCI using multiple ERP components associated to facial emotion processingBrain-Computer Interface Research, SpringBriefs in Electrical and Computer Engineering, 61-72, 2013. [Link]
  20. Y. Zhang, Q. Zhao, G. Zhou, X. Wang, A. Cichocki. Regularized CSP with Fisher’s criterion to improve classification of single-trial ERPs for BCIIn: 9th IEEE International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2012), 891-895, Chongqing, China, 29-31 May 2012. [PDF] [Link]
  21. G. Zhou, Z. He, Y. Zhang, Q. Zhao, A. Cichocki. Canonical polyadic decomposition: From 3-way to N-wayIn: 8th IEEE International Conference on Computional Intelligence and Security (CIS 2012), 391-395, Guangzhou, China, 17-18 November 2012. [Link]
  22. Y. Zhang, G. Zhou, Q. Zhao, A. Onishi, J. Jin, X. Wang, A. Cichocki. Multiway canonical correlation analysis for frequency components recognition in SSVEP-based BCIsIn: 18th International Conference on Neural Information Processing 2011 (ICONIP 2011), Lecture Notes in Computer Science (LNCS), Springer, 7062: 287-295, Shanghai, China, 14-17 November 2011. [PDF] [Link]
  23. J. Jin, Y. Zhang, X. Wang. A novel combination of time phase and EEG frequency components for SSVEP-based BCIIn: 18th International Conference on Neural Information Processing 2011 (ICONIP 2011), Lecture Notes in Computer Science (LNCS), Springer, 7062: 273-278, Shanghai, China, 14-17 November 2011. [Link]
  24. A. Onishi, Y. Zhang, Q. Zhao, A. Cichocki. Fast and reliable P300-based BCI with facial imagesIn: 5th International Brain-Computer Interface Conference, 191-195, Graz, Austria, 22-24 September 2011. [Link]