Sparse Bayesian Classification of EEG for Brain–Computer Interface
暂无分享,去创建一个
Xingyu Wang | Andrzej Cichocki | Qibin Zhao | Yu Zhang | Guoxu Zhou | Jing Jin | A. Cichocki | Xingyu Wang | Jing Jin | Yu Zhang | Guoxu Zhou | Qibin Zhao
[1] Haiping Lu,et al. Regularized Common Spatial Pattern With Aggregation for EEG Classification in Small-Sample Setting , 2010, IEEE Transactions on Biomedical Engineering.
[2] Wei Wu,et al. Probabilistic Common Spatial Patterns for Multichannel EEG Analysis , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[3] Liqing Zhang,et al. Bayesian CP Factorization of Incomplete Tensors with Automatic Rank Determination , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[4] Wei Wu,et al. Bayesian estimation of ERP components from multicondition and multichannel EEG , 2014, NeuroImage.
[5] Wei Wu,et al. A hierarchical Bayesian approach for learning sparse spatio-temporal decompositions of multichannel EEG , 2011, NeuroImage.
[6] E. Donchin,et al. A P300-based brain–computer interface: Initial tests by ALS patients , 2006, Clinical Neurophysiology.
[7] Florian Steinke,et al. Experimental design for efficient identification of gene regulatory networks using sparse Bayesian models , 2006, BMC Systems Biology.
[8] Xingyu Wang,et al. Spatial-Temporal Discriminant Analysis for ERP-Based Brain-Computer Interface , 2013, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[9] A. N. Tikhonov,et al. Solutions of ill-posed problems , 1977 .
[10] Marc M. Van Hulle,et al. Comparison of Classification Methods for P300 Brain-Computer Interface on Disabled Subjects , 2011, Comput. Intell. Neurosci..
[11] Xingyu Wang,et al. An ERP-Based BCI using an oddball Paradigm with Different Faces and Reduced errors in Critical Functions , 2014, Int. J. Neural Syst..
[12] Touradj Ebrahimi,et al. Bayesian feature selection applied in a p300 brain-computer interface , 2008, 2008 16th European Signal Processing Conference.
[13] Radford M. Neal. Pattern Recognition and Machine Learning , 2007, Technometrics.
[14] Aggelos K. Katsaggelos,et al. Bayesian Compressive Sensing Using Laplace Priors , 2010, IEEE Transactions on Image Processing.
[15] Stefan Haufe,et al. Single-trial analysis and classification of ERP components — A tutorial , 2011, NeuroImage.
[16] Touradj Ebrahimi,et al. An efficient P300-based brain–computer interface for disabled subjects , 2008, Journal of Neuroscience Methods.
[17] Matthias W. Seeger,et al. Compressed sensing and Bayesian experimental design , 2008, ICML '08.
[18] Cuntai Guan,et al. Optimizing Spatial Filters by Minimizing Within-Class Dissimilarities in Electroencephalogram-Based Brain–Computer Interface , 2013, IEEE Transactions on Neural Networks and Learning Systems.
[19] Yong Xiang,et al. Projection-Pursuit-Based Method for Blind Separation of Nonnegative Sources , 2013, IEEE Transactions on Neural Networks and Learning Systems.
[20] Robert H. Halstead,et al. Matrix Computations , 2011, Encyclopedia of Parallel Computing.
[21] Clemens Brunner,et al. Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks , 2006, NeuroImage.
[22] Cuntai Guan,et al. Filter Bank Common Spatial Pattern (FBCSP) in Brain-Computer Interface , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).
[23] Yuanqing Li,et al. A self-training semi-supervised SVM algorithm and its application in an EEG-based brain computer interface speller system , 2008, Pattern Recognit. Lett..
[24] Lawrence Carin,et al. Bayesian Compressive Sensing , 2008, IEEE Transactions on Signal Processing.
[25] N. L. Johnson,et al. Multivariate Analysis , 1958, Nature.
[26] Gabriel Curio,et al. MACHINE LEARNING TECHNIQUES FOR BRAIN-COMPUTER INTERFACES , 2004 .
[27] George Eastman House,et al. Sparse Bayesian Learning and the Relevance Vector Machine , 2001 .
[28] Xingyu Wang,et al. Author's Personal Copy Biomedical Signal Processing and Control Lasso Based Stimulus Frequency Recognition Model for Ssvep Bcis , 2022 .
[29] Mário A. T. Figueiredo. Adaptive Sparseness for Supervised Learning , 2003, IEEE Trans. Pattern Anal. Mach. Intell..
[30] Yuanqing Li,et al. Joint feature re-extraction and classification using an iterative semi-supervised support vector machine algorithm , 2008, Machine Learning.
[31] A. Cichocki,et al. An optimized ERP brain–computer interface based on facial expression changes , 2014, Journal of neural engineering.
[32] R Thull,et al. Vergleichende Untersuchungen zur Eignung eines neuen Oberflächenkonditionierungsverfahrens (Airsonic Mini Sandblaster®) in der Klebebrückentechnik / Comparative Studies on the Applicability of a New Surface Conditioning System (Airsonic Mini Sandblaster®) in Adhesive Bridging Technic , 2004, Biomedizinische Technik. Biomedical engineering.
[33] Tao Liu,et al. N200-speller using motion-onset visual response , 2009, Clinical Neurophysiology.
[34] Cuntai Guan,et al. Optimizing the Channel Selection and Classification Accuracy in EEG-Based BCI , 2011, IEEE Transactions on Biomedical Engineering.
[35] Bhaskar D. Rao,et al. Performance Evaluation of Latent Variable Models with Sparse Priors , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.
[36] David J. C. MacKay,et al. Bayesian Interpolation , 1992, Neural Computation.
[37] Cuntai Guan,et al. Bayesian Learning for Spatial Filtering in an EEG-Based Brain–Computer Interface , 2013, IEEE Transactions on Neural Networks and Learning Systems.
[38] Yuanqing Li,et al. Blind estimation of channel parameters and source components for EEG signals: a sparse factorization approach , 2006, IEEE Transactions on Neural Networks.
[39] D. Yao,et al. An Enhanced Probabilistic LDA for Multi-Class Brain Computer Interface , 2011, PloS one.
[40] Chiew Tong Lau,et al. A New Discriminative Common Spatial Pattern Method for Motor Imagery Brain–Computer Interfaces , 2009, IEEE Transactions on Biomedical Engineering.
[41] Michael E. Tipping. Bayesian Inference: An Introduction to Principles and Practice in Machine Learning , 2003, Advanced Lectures on Machine Learning.
[42] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[43] Andrzej Cichocki,et al. L1-Regularized Multiway Canonical Correlation Analysis for SSVEP-Based BCI , 2013, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[44] K.-R. Muller,et al. Optimizing Spatial filters for Robust EEG Single-Trial Analysis , 2008, IEEE Signal Processing Magazine.
[45] J. Wolpaw,et al. A novel P300-based brain–computer interface stimulus presentation paradigm: Moving beyond rows and columns , 2010, Clinical Neurophysiology.
[46] Yong Xiang,et al. Time-Frequency Approach to Underdetermined Blind Source Separation , 2012, IEEE Transactions on Neural Networks and Learning Systems.
[47] Jukka Heikkonen,et al. A local neural classifier for the recognition of EEG patterns associated to mental tasks , 2002, IEEE Trans. Neural Networks.
[48] Klaus-Robert Müller,et al. Classifying Single Trial EEG: Towards Brain Computer Interfacing , 2001, NIPS.
[49] Saeid Sanei,et al. Adaptive Processing of Brain Signals , 2013 .
[50] Andrzej Cichocki,et al. Accelerated Canonical Polyadic Decomposition Using Mode Reduction , 2012, IEEE Transactions on Neural Networks and Learning Systems.
[51] Xingyu Wang,et al. A P300 Brain-Computer Interface Based on a Modification of the Mismatch Negativity Paradigm , 2015, Int. J. Neural Syst..
[52] Zhaoshui He,et al. Minimum-Volume-Constrained Nonnegative Matrix Factorization: Enhanced Ability of Learning Parts , 2011, IEEE Transactions on Neural Networks.
[53] Bhaskar D. Rao,et al. Sparse Signal Recovery With Temporally Correlated Source Vectors Using Sparse Bayesian Learning , 2011, IEEE Journal of Selected Topics in Signal Processing.
[54] A. Cichocki,et al. A novel BCI based on ERP components sensitive to configural processing of human faces , 2012, Journal of neural engineering.
[55] Helge J. Ritter,et al. BCI competition 2003-data set IIb: support vector machines for the P300 speller paradigm , 2004, IEEE Transactions on Biomedical Engineering.
[56] Dennis J. McFarland,et al. Brain–computer interfaces for communication and control , 2002, Clinical Neurophysiology.
[57] Seungjin Choi,et al. Bayesian common spatial patterns for multi-subject EEG classification , 2014, Neural Networks.
[58] Liqing Zhang,et al. Bayesian Robust Tensor Factorization for Incomplete Multiway Data , 2014, IEEE Transactions on Neural Networks and Learning Systems.
[59] Xingyu Wang,et al. Aggregation of Sparse Linear Discriminant analyses for Event-Related potential Classification in Brain-Computer Interface , 2014, Int. J. Neural Syst..
[60] M Congedo,et al. A review of classification algorithms for EEG-based brain–computer interfaces , 2007, Journal of neural engineering.
[61] Xingyu Wang,et al. Optimizing spatial patterns with sparse filter bands for motor-imagery based brain–computer interface , 2015, Journal of Neuroscience Methods.
[62] Xingyu Wang,et al. Fast nonnegative tensor factorization based on accelerated proximal gradient and low-rank approximation , 2016, Neurocomputing.
[63] Wei Wu,et al. RSTFC: A Novel Algorithm for Spatio-Temporal Filtering and Classification of Single-Trial EEG , 2015, IEEE Transactions on Neural Networks and Learning Systems.
[64] Dean J Krusienski,et al. A comparison of classification techniques for the P300 Speller , 2006, Journal of neural engineering.
[65] Ping Yang,et al. An Empirical Bayesian Framework for Brain–Computer Interfaces , 2009, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[66] Xingyu Wang,et al. Frequency Recognition in SSVEP-Based BCI using Multiset Canonical Correlation Analysis , 2013, Int. J. Neural Syst..