Temporally Constrained Sparse Group Spatial Patterns for Motor Imagery BCI
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Xingyu Wang | Andrzej Cichocki | Yu Zhang | Guoxu Zhou | Jing Jin | Chang S Nam | A. Cichocki | Xingyu Wang | Jing Jin | Yu Zhang | Guoxu Zhou | C. Nam
[1] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[2] Yurii Nesterov,et al. Introductory Lectures on Convex Optimization - A Basic Course , 2014, Applied Optimization.
[3] Gert Pfurtscheller,et al. Characterization of four-class motor imagery EEG data for the BCI-competition 2005 , 2005, Journal of neural engineering.
[4] R. Tibshirani,et al. Sparsity and smoothness via the fused lasso , 2005 .
[5] Klaus-Robert Müller,et al. Spatio-spectral filters for improving the classification of single trial EEG , 2005, IEEE Transactions on Biomedical Engineering.
[6] Le Song,et al. Classifying EEG for Brain-Computer Interface: Learning Optimal Filters for Dynamical System Features , 2006, ICML.
[7] Klaus-Robert Müller,et al. Combined Optimization of Spatial and Temporal Filters for Improving Brain-Computer Interfacing , 2006, IEEE Transactions on Biomedical Engineering.
[8] Clemens Brunner,et al. Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks , 2006, NeuroImage.
[9] Zhongming Liu,et al. An enhanced time-frequency-spatial approach for motor imagery classification , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[10] M Congedo,et al. A review of classification algorithms for EEG-based brain–computer interfaces , 2007, Journal of neural engineering.
[11] Ping Xue,et al. Sub-band Common Spatial Pattern (SBCSP) for Brain-Computer Interface , 2007, 2007 3rd International IEEE/EMBS Conference on Neural Engineering.
[12] K.-R. Muller,et al. Optimizing Spatial filters for Robust EEG Single-Trial Analysis , 2008, IEEE Signal Processing Magazine.
[13] Chiew Tong Lau,et al. A New Discriminative Common Spatial Pattern Method for Motor Imagery Brain–Computer Interfaces , 2009, IEEE Transactions on Biomedical Engineering.
[14] Jieping Ye,et al. Multi-Task Feature Learning Via Efficient l2, 1-Norm Minimization , 2009, UAI.
[15] Jieping Ye,et al. An efficient algorithm for a class of fused lasso problems , 2010, KDD.
[16] Haiping Lu,et al. Regularized Common Spatial Pattern With Aggregation for EEG Classification in Small-Sample Setting , 2010, IEEE Transactions on Biomedical Engineering.
[17] R. Tibshirani,et al. A note on the group lasso and a sparse group lasso , 2010, 1001.0736.
[18] G. Pfurtscheller,et al. Self-Paced Operation of an SSVEP-Based Orthosis With and Without an Imagery-Based “Brain Switch:” A Feasibility Study Towards a Hybrid BCI , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[19] 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.
[20] Cuntai Guan,et al. Regularizing Common Spatial Patterns to Improve BCI Designs: Unified Theory and New Algorithms , 2011, IEEE Transactions on Biomedical Engineering.
[21] Wei Wu,et al. A hierarchical Bayesian approach for learning sparse spatio-temporal decompositions of multichannel EEG , 2011, NeuroImage.
[22] Moritz Grosse-Wentrup,et al. Critical issues in state-of-the-art brain–computer interface signal processing , 2011, Journal of neural engineering.
[23] Cuntai Guan,et al. Mutual information-based selection of optimal spatial-temporal patterns for single-trial EEG-based BCIs , 2012, Pattern Recognit..
[24] A. Cichocki,et al. A novel BCI based on ERP components sensitive to configural processing of human faces , 2012, Journal of neural engineering.
[25] Jiayu Zhou,et al. Modeling disease progression via fused sparse group lasso , 2012, KDD.
[26] Cuntai Guan,et al. Filter Bank Common Spatial Pattern Algorithm on BCI Competition IV Datasets 2a and 2b , 2012, Front. Neurosci..
[27] Yalda Mohsenzadeh,et al. The Relevance Sample-Feature Machine: A Sparse Bayesian Learning Approach to Joint Feature-Sample Selection , 2013, IEEE Transactions on Cybernetics.
[28] 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..
[29] Toshihisa Tanaka,et al. Common Spatio-Time-Frequency Patterns for Motor Imagery-Based Brain Machine Interfaces , 2013, Comput. Intell. Neurosci..
[30] Feng Li,et al. Discrimination Between Control and Idle States in Asynchronous SSVEP-Based Brain Switches: A Pseudo-Key-Based Approach , 2013, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[31] Andrzej Cichocki,et al. L1-Regularized Multiway Canonical Correlation Analysis for SSVEP-Based BCI , 2013, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[32] Toshihisa Tanaka,et al. Simultaneous Design of FIR Filter Banks and Spatial Patterns for EEG Signal Classification , 2013, IEEE Transactions on Biomedical Engineering.
[33] Xingyu Wang,et al. Spatial-Temporal Discriminant Analysis for ERP-Based Brain-Computer Interface , 2013, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[34] Bin He,et al. Brain–Computer Interfaces Using Sensorimotor Rhythms: Current State and Future Perspectives , 2014, IEEE Transactions on Biomedical Engineering.
[35] Yong Xu,et al. Sparse Representation for Brain Signal Processing: A tutorial on methods and applications , 2014, IEEE Signal Processing Magazine.
[36] Xingyu Wang,et al. Frequency Recognition in SSVEP-Based BCI using Multiset Canonical Correlation Analysis , 2013, Int. J. Neural Syst..
[37] Xingyu Wang,et al. Aggregation of Sparse Linear Discriminant analyses for Event-Related potential Classification in Brain-Computer Interface , 2014, Int. J. Neural Syst..
[38] Yuanqing Li,et al. Grouped Automatic Relevance Determination and Its Application in Channel Selection for P300 BCIs , 2015, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[39] Xiangjian He,et al. Robust visual tracking via efficient manifold ranking with low-dimensional compressive features , 2015, Pattern Recognit..
[40] Wei Wu. Probabilistic Common Spatial Patterns for Multichannel EEG Analysis , 2015 .
[41] Xingyu Wang,et al. A P300 Brain-Computer Interface Based on a Modification of the Mismatch Negativity Paradigm , 2015, Int. J. Neural Syst..
[42] Cuntai Guan,et al. Brain–Computer Interface for Neurorehabilitation of Upper Limb After Stroke , 2015, Proceedings of the IEEE.
[43] Xingyu Wang,et al. Optimizing spatial patterns with sparse filter bands for motor-imagery based brain–computer interface , 2015, Journal of Neuroscience Methods.
[44] Xuelong Li,et al. Block-Row Sparse Multiview Multilabel Learning for Image Classification , 2016, IEEE Transactions on Cybernetics.
[45] Andrea Petracca,et al. A Classification Algorithm for Electroencephalography Signals by Self-Induced Emotional Stimuli , 2016, IEEE Transactions on Cybernetics.
[46] Andrzej Cichocki,et al. Group Component Analysis for Multiblock Data: Common and Individual Feature Extraction , 2012, IEEE Transactions on Neural Networks and Learning Systems.
[47] Xingyu Wang,et al. Sparse Bayesian Classification of EEG for Brain–Computer Interface , 2016, IEEE Transactions on Neural Networks and Learning Systems.
[48] Daoqiang Zhang,et al. Pairwise Constraint-Guided Sparse Learning for Feature Selection , 2016, IEEE Transactions on Cybernetics.
[49] Xingyu Wang,et al. Discriminative Feature Extraction via Multivariate Linear Regression for SSVEP-Based BCI , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[50] N. Birbaumer,et al. Brain–computer interfaces for communication and rehabilitation , 2016, Nature Reviews Neurology.
[51] Andrzej Cichocki,et al. Linked Component Analysis From Matrices to High-Order Tensors: Applications to Biomedical Data , 2015, Proceedings of the IEEE.
[52] Soo-Young Lee,et al. EEG-Based Classification of Implicit Intention During Self-Relevant Sentence Reading , 2016, IEEE Transactions on Cybernetics.
[53] Toshihisa Tanaka,et al. Multilinear Discriminant Analysis With Subspace Constraints for Single-Trial Classification of Event-Related Potentials , 2016, IEEE Journal of Selected Topics in Signal Processing.
[54] Qinghua Hu,et al. Flexible Multi-View Dimensionality Co-Reduction , 2017, IEEE Transactions on Image Processing.
[55] Xingyu Wang,et al. Sparse Bayesian multiway canonical correlation analysis for EEG pattern recognition , 2017, Neurocomputing.
[56] Shichao Zhang,et al. Robust Joint Graph Sparse Coding for Unsupervised Spectral Feature Selection , 2017, IEEE Transactions on Neural Networks and Learning Systems.
[57] Isabelle Bloch,et al. Subject-specific time-frequency selection for multi-class motor imagery-based BCIs using few Laplacian EEG channels , 2017, Biomed. Signal Process. Control..
[58] Yuan Yan Tang,et al. Weighted Joint Sparse Representation for Removing Mixed Noise in Image , 2017, IEEE Transactions on Cybernetics.
[59] F. Yger,et al. Riemannian Approaches in Brain-Computer Interfaces: A Review , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[60] Xingyu Wang,et al. Sparse Bayesian Learning for Obtaining Sparsity of EEG Frequency Bands Based Feature Vectors in Motor Imagery Classification , 2017, Int. J. Neural Syst..
[61] Dong Ni,et al. Relational-Regularized Discriminative Sparse Learning for Alzheimer’s Disease Diagnosis , 2017, IEEE Transactions on Cybernetics.
[62] Cuntai Guan,et al. EEG-Based Strategies to Detect Motor Imagery for Control and Rehabilitation , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[63] Licheng Jiao,et al. Discriminative Dictionary Learning With Two-Level Low Rank and Group Sparse Decomposition for Image Classification , 2017, IEEE Transactions on Cybernetics.
[64] Dinggang Shen,et al. Medical Image Synthesis with Deep Convolutional Adversarial Networks , 2018, IEEE Transactions on Biomedical Engineering.
[65] Yu Zhang,et al. A Novel Multilayer Correlation Maximization Model for Improving CCA-Based Frequency Recognition in SSVEP Brain-Computer Interface , 2017, Int. J. Neural Syst..
[66] Jie Yang,et al. Robust Visual Tracking via Online Discriminative and Low-Rank Dictionary Learning , 2018, IEEE Transactions on Cybernetics.
[67] Yu Zhang,et al. Multi-kernel extreme learning machine for EEG classification in brain-computer interfaces , 2018, Expert Syst. Appl..
[68] Li Wang,et al. Longitudinally Guided Super-Resolution of Neonatal Brain Magnetic Resonance Images , 2019, IEEE Transactions on Cybernetics.
[69] Ehsan Adeli,et al. 3-D Fully Convolutional Networks for Multimodal Isointense Infant Brain Image Segmentation , 2019, IEEE Transactions on Cybernetics.
[70] Dinggang Shen,et al. Strength and similarity guided group-level brain functional network construction for MCI diagnosis , 2019, Pattern Recognit..
[71] Yu Zhang,et al. Sparse Group Representation Model for Motor Imagery EEG Classification , 2019, IEEE Journal of Biomedical and Health Informatics.