Sparse Bayesian Learning for Obtaining Sparsity of EEG Frequency Bands Based Feature Vectors in Motor Imagery Classification
暂无分享,去创建一个
Xingyu Wang | Yu Wang | Yu Zhang | Jing Jin | Xingyu Wang | Jing Jin | Yu Zhang | Yu Wang
[1] Zhihan Lv,et al. Multi-dimensional visualization of large-scale marine hydrological environmental data , 2016, Adv. Eng. Softw..
[2] Michael E. Tipping. Sparse Bayesian Learning and the Relevance Vector Machine , 2001, J. Mach. Learn. Res..
[3] Wei Wu,et al. STRAPS: A Fully Data-Driven Spatio-Temporally Regularized Algorithm for M/EEG Patch Source Imaging , 2015, Int. J. Neural Syst..
[4] H. Adeli,et al. Fractality analysis of frontal brain in major depressive disorder. , 2012, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.
[5] J J Vidal,et al. Toward direct brain-computer communication. , 1973, Annual review of biophysics and bioengineering.
[6] Chiew Tong Lau,et al. A New Discriminative Common Spatial Pattern Method for Motor Imagery Brain–Computer Interfaces , 2009, IEEE Transactions on Biomedical Engineering.
[7] Stefan Haufe,et al. Single-trial analysis and classification of ERP components — A tutorial , 2011, NeuroImage.
[8] Wei-Yen Hsu,et al. Single-Trial Motor Imagery Classification using Asymmetry Ratio, phase Relation, Wavelet-Based Fractal, and their Selected Combination , 2013, Int. J. Neural Syst..
[9] Hojjat Adeli,et al. Enhanced probabilistic neural network with local decision circles: A robust classifier , 2010, Integr. Comput. Aided Eng..
[10] H. Adeli,et al. EEG/MEG- and imaging-based diagnosis of Alzheimer’s disease , 2013, Reviews in the neurosciences.
[11] Fumitoshi Matsuno,et al. A Novel EOG/EEG Hybrid Human–Machine Interface Adopting Eye Movements and ERPs: Application to Robot Control , 2015, IEEE Transactions on Biomedical Engineering.
[12] J. Buford,et al. Wavelet methodology to improve single unit isolation in primary motor cortex cells , 2015, Journal of Neuroscience Methods.
[13] 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.
[14] A. Cichocki,et al. A novel BCI based on ERP components sensitive to configural processing of human faces , 2012, Journal of neural engineering.
[15] Aggelos K. Katsaggelos,et al. Bayesian Compressive Sensing Using Laplace Priors , 2010, IEEE Transactions on Image Processing.
[16] J. S. Rao,et al. Spike and slab variable selection: Frequentist and Bayesian strategies , 2005, math/0505633.
[17] Dezhong Yao,et al. Lp Norm Iterative Sparse Solution for EEG Source Localization , 2007, IEEE Transactions on Biomedical Engineering.
[18] Michael E. Tipping. Bayesian Inference: An Introduction to Principles and Practice in Machine Learning , 2003, Advanced Lectures on Machine Learning.
[19] Wei Wu,et al. An Idle-State Detection Algorithm for SSVEP-Based Brain-Computer Interfaces Using a Maximum Evoked Response Spatial Filter , 2015, Int. J. Neural Syst..
[20] Hojjat Adeli,et al. Probabilistic neural networks for diagnosis of Alzheimer's disease using conventional and wavelet coherence , 2011, Journal of Neuroscience Methods.
[21] Hojjat Adeli,et al. Fractality and a Wavelet-Chaos-Neural Network Methodology for EEG-Based Diagnosis of Autistic Spectrum Disorder , 2010, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.
[22] Hojjat Adeli,et al. Functional community analysis of brain: A new approach for EEG-based investigation of the brain pathology , 2011, NeuroImage.
[23] Xingyu Wang,et al. Aggregation of Sparse Linear Discriminant analyses for Event-Related potential Classification in Brain-Computer Interface , 2014, Int. J. Neural Syst..
[24] Klaus-Robert Müller,et al. Combined Optimization of Spatial and Temporal Filters for Improving Brain-Computer Interfacing , 2006, IEEE Transactions on Biomedical Engineering.
[25] Andrzej Cichocki,et al. L1-Regularized Multiway Canonical Correlation Analysis for SSVEP-Based BCI , 2013, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[26] M Congedo,et al. A review of classification algorithms for EEG-based brain–computer interfaces , 2007, Journal of neural engineering.
[27] Hojjat Adeli,et al. Wavelet-Synchronization Methodology: A New Approach for EEG-Based Diagnosis of ADHD , 2010, Clinical EEG and neuroscience.
[28] H. Adeli,et al. Visibility graph similarity: A new measure of generalized synchronization in coupled dynamic systems , 2012 .
[29] Andrzej Cichocki,et al. Linked Component Analysis From Matrices to High-Order Tensors: Applications to Biomedical Data , 2015, Proceedings of the IEEE.
[30] H. Adeli,et al. Graph Theoretical Analysis of Organization of Functional Brain Networks in ADHD , 2012, Clinical EEG and neuroscience.
[31] Xingyu Wang,et al. Discriminative Feature Extraction via Multivariate Linear Regression for SSVEP-Based BCI , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[32] Ana Belén Moreno,et al. Rician noise attenuation in the wavelet packet transformed domain for brain MRI , 2014, Integr. Comput. Aided Eng..
[33] Clemens Brunner,et al. Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks , 2006, NeuroImage.
[34] Cuntai Guan,et al. Regularizing Common Spatial Patterns to Improve BCI Designs: Unified Theory and New Algorithms , 2011, IEEE Transactions on Biomedical Engineering.
[35] Yu Zhang,et al. An adaptive neural network approach for operator functional state prediction using psychophysiological data , 2015, Integr. Comput. Aided Eng..
[36] E. Donchin,et al. Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. , 1988, Electroencephalography and clinical neurophysiology.
[37] Hojjat Adeli,et al. Fuzzy Synchronization Likelihood-wavelet methodology for diagnosis of autism spectrum disorder , 2012, Journal of Neuroscience Methods.
[38] G. Pfurtscheller,et al. Optimal spatial filtering of single trial EEG during imagined hand movement. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.
[39] H. Adeli,et al. Brain-computer interface technologies: from signal to action , 2013, Reviews in the neurosciences.
[40] Gert Pfurtscheller,et al. Motor imagery and direct brain-computer communication , 2001, Proc. IEEE.
[41] Xingyu Wang,et al. SSVEP recognition using common feature analysis in brain–computer interface , 2015, Journal of Neuroscience Methods.
[42] Xingyu Wang,et al. Author's Personal Copy Biomedical Signal Processing and Control Lasso Based Stimulus Frequency Recognition Model for Ssvep Bcis , 2022 .
[43] Mário A. T. Figueiredo. Adaptive Sparseness for Supervised Learning , 2003, IEEE Trans. Pattern Anal. Mach. Intell..
[44] Xingyu Wang,et al. Optimizing spatial patterns with sparse filter bands for motor-imagery based brain–computer interface , 2015, Journal of Neuroscience Methods.
[45] 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..
[46] Xingyu Wang,et al. A P300 Brain-Computer Interface Based on a Modification of the Mismatch Negativity Paradigm , 2015, Int. J. Neural Syst..
[47] Cuntai Guan,et al. Optimizing the Channel Selection and Classification Accuracy in EEG-Based BCI , 2011, IEEE Transactions on Biomedical Engineering.
[48] Wei Wu,et al. Probabilistic Common Spatial Patterns for Multichannel EEG Analysis , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[49] J. Buford,et al. Combined corticospinal and reticulospinal effects on upper limb muscles , 2014, Neuroscience Letters.
[50] Xingyu Wang,et al. Spatial-Temporal Discriminant Analysis for ERP-Based Brain-Computer Interface , 2013, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[51] Trevor Hastie,et al. Regularization Paths for Generalized Linear Models via Coordinate Descent. , 2010, Journal of statistical software.
[52] Xingyu Wang,et al. Frequency Recognition in SSVEP-Based BCI using Multiset Canonical Correlation Analysis , 2013, Int. J. Neural Syst..
[53] Tapani Ristaniemi,et al. Single-Trial Based Independent Component Analysis on mismatch Negativity in Children , 2010, Int. J. Neural Syst..
[54] Haiping Lu,et al. Regularized Common Spatial Pattern With Aggregation for EEG Classification in Small-Sample Setting , 2010, IEEE Transactions on Biomedical Engineering.
[55] Wei Wu,et al. A hierarchical Bayesian approach for learning sparse spatio-temporal decompositions of multichannel EEG , 2011, NeuroImage.
[56] J. Buford,et al. Brain–Computer Interface after Nervous System Injury , 2014, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.
[57] Hojjat Adeli,et al. Fuzzy Synchronization Likelihood with Application to Attention-Deficit/Hyperactivity Disorder , 2011, Clinical EEG and neuroscience.
[58] H. Adeli,et al. Complexity of functional connectivity networks in mild cognitive impairment subjects during a working memory task , 2014, Clinical Neurophysiology.
[59] Udaya B. Kogalur,et al. spikeslab: Prediction and Variable Selection Using Spike and Slab Regression , 2010, R J..
[60] Tapani Ristaniemi,et al. Multi-Domain Feature Extraction for Small Event-Related potentials through Nonnegative Multi-Way Array Decomposition from Low Dense Array EEG , 2013, Int. J. Neural Syst..
[61] Jie Li,et al. Design of assistive Wheelchair System directly Steered by Human Thoughts , 2013, Int. J. Neural Syst..
[62] Wei-Yen Hsu,et al. Continuous EEG Signal Analysis for Asynchronous BCI Application , 2011, Int. J. Neural Syst..
[63] G. Pfurtscheller,et al. Brain-Computer Interfaces for Communication and Control. , 2011, Communications of the ACM.
[64] Xingyu Wang,et al. Fast nonnegative tensor factorization based on accelerated proximal gradient and low-rank approximation , 2016, Neurocomputing.
[65] Qibin Zhao,et al. Uncorrelated Multiway Discriminant Analysis for Motor Imagery EEG Classification , 2015, Int. J. Neural Syst..
[66] Klaus-Robert Müller,et al. Spatio-spectral filters for improving the classification of single trial EEG , 2005, IEEE Transactions on Biomedical Engineering.
[67] David J. C. MacKay,et al. Bayesian Interpolation , 1992, Neural Computation.
[68] Toshihisa Tanaka,et al. Simultaneous Design of FIR Filter Banks and Spatial Patterns for EEG Signal Classification , 2013, IEEE Transactions on Biomedical Engineering.
[69] Heikki Lyytinen,et al. Benefits of Multi-Domain Feature of mismatch Negativity Extracted by Non-Negative Tensor Factorization from EEG Collected by Low-Density Array , 2012, Int. J. Neural Syst..
[70] 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..
[71] Wei Wu,et al. Bayesian estimation of ERP components from multicondition and multichannel EEG , 2014, NeuroImage.
[72] Hojjat Adeli,et al. New diagnostic EEG markers of the Alzheimer’s disease using visibility graph , 2010, Journal of Neural Transmission.
[73] J.J. Vidal,et al. Real-time detection of brain events in EEG , 1977, Proceedings of the IEEE.
[74] H. Adeli,et al. A spatio-temporal wavelet-chaos methodology for EEG-based diagnosis of Alzheimer's disease , 2008, Neuroscience Letters.
[75] H. Adeli,et al. Fractality and a Wavelet-chaos-Methodology for EEG-based Diagnosis of Alzheimer Disease , 2011, Alzheimer disease and associated disorders.