These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
210 related articles for article (PubMed ID: 26415189)
1. Sparse Bayesian Classification of EEG for Brain-Computer Interface. Zhang Y; Zhou G; Jin J; Zhao Q; Wang X; Cichocki A IEEE Trans Neural Netw Learn Syst; 2016 Nov; 27(11):2256-2267. PubMed ID: 26415189 [TBL] [Abstract][Full Text] [Related]
2. RSTFC: A Novel Algorithm for Spatio-Temporal Filtering and Classification of Single-Trial EEG. Qi F; Li Y; Wu W IEEE Trans Neural Netw Learn Syst; 2015 Dec; 26(12):3070-82. PubMed ID: 25730834 [TBL] [Abstract][Full Text] [Related]
3. Sparse Bayesian Learning for Obtaining Sparsity of EEG Frequency Bands Based Feature Vectors in Motor Imagery Classification. Zhang Y; Wang Y; Jin J; Wang X Int J Neural Syst; 2017 Mar; 27(2):1650032. PubMed ID: 27377661 [TBL] [Abstract][Full Text] [Related]
4. Sparse Kernel Machines for motor imagery EEG classification. Oikonomou VP; Nikolopoulos S; Petrantonakis P; Kompatsiaris I Annu Int Conf IEEE Eng Med Biol Soc; 2018 Jul; 2018():207-210. PubMed ID: 30440374 [TBL] [Abstract][Full Text] [Related]
5. Sparse Bayesian Learning for End-to-End EEG Decoding. Wang W; Qi F; Wipf DP; Cai C; Yu T; Li Y; Zhang Y; Yu Z; Wu W IEEE Trans Pattern Anal Mach Intell; 2023 Dec; 45(12):15632-15649. PubMed ID: 37506000 [TBL] [Abstract][Full Text] [Related]
6. A Quasi-probabilistic distribution model for EEG Signal classification by using 2-D signal representation. Murat Yilmaz C; Kose C; Hatipoglu B Comput Methods Programs Biomed; 2018 Aug; 162():187-196. PubMed ID: 29903485 [TBL] [Abstract][Full Text] [Related]
7. Simple adaptive sparse representation based classification schemes for EEG based brain-computer interface applications. Shin Y; Lee S; Ahn M; Cho H; Jun SC; Lee HN Comput Biol Med; 2015 Nov; 66():29-38. PubMed ID: 26378500 [TBL] [Abstract][Full Text] [Related]
8. Gene selection in cancer classification using sparse logistic regression with Bayesian regularization. Cawley GC; Talbot NL Bioinformatics; 2006 Oct; 22(19):2348-55. PubMed ID: 16844704 [TBL] [Abstract][Full Text] [Related]
9. A review of classification algorithms for EEG-based brain-computer interfaces: a 10 year update. Lotte F; Bougrain L; Cichocki A; Clerc M; Congedo M; Rakotomamonjy A; Yger F J Neural Eng; 2018 Jun; 15(3):031005. PubMed ID: 29488902 [TBL] [Abstract][Full Text] [Related]
11. Bayesian learning for spatial filtering in an EEG-based brain-computer interface. Zhang H; Yang H; Guan C IEEE Trans Neural Netw Learn Syst; 2013 Jul; 24(7):1049-60. PubMed ID: 24808520 [TBL] [Abstract][Full Text] [Related]
12. A spatial-frequency-temporal optimized feature sparse representation-based classification method for motor imagery EEG pattern recognition. Miao M; Wang A; Liu F Med Biol Eng Comput; 2017 Sep; 55(9):1589-1603. PubMed ID: 28161876 [TBL] [Abstract][Full Text] [Related]
13. Aggregation of sparse linear discriminant analyses for event-related potential classification in brain-computer interface. Zhang Y; Zhou G; Jin J; Zhao Q; Wang X; Cichocki A Int J Neural Syst; 2014 Feb; 24(1):1450003. PubMed ID: 24344691 [TBL] [Abstract][Full Text] [Related]
14. A comparison of classification methods for recognizing single-trial P300 in brain-computer interfaces Xiao X; Xu M; Wang Y; Jung TP; Ming D Annu Int Conf IEEE Eng Med Biol Soc; 2019 Jul; 2019():3032-3035. PubMed ID: 31946527 [TBL] [Abstract][Full Text] [Related]
15. A prior neurophysiologic knowledge free tensor-based scheme for single trial EEG classification. Li J; Zhang L; Tao D; Sun H; Zhao Q IEEE Trans Neural Syst Rehabil Eng; 2009 Apr; 17(2):107-15. PubMed ID: 19273039 [TBL] [Abstract][Full Text] [Related]
16. Bayesian Uncertainty Modeling for P300-Based Brain-Computer Interface. Ma R; Zhang H; Zhang J; Zhong X; Yu Z; Li Y; Yu T; Gu Z IEEE Trans Neural Syst Rehabil Eng; 2023; 31():2789-2799. PubMed ID: 37318970 [TBL] [Abstract][Full Text] [Related]
17. Grouped Automatic Relevance Determination and Its Application in Channel Selection for P300 BCIs. Yu T; Yu Z; Gu Z; Li Y IEEE Trans Neural Syst Rehabil Eng; 2015 Nov; 23(6):1068-77. PubMed ID: 25794393 [TBL] [Abstract][Full Text] [Related]
18. Comparison of linear, nonlinear, and feature selection methods for EEG signal classification. Garrett D; Peterson DA; Anderson CW; Thaut MH IEEE Trans Neural Syst Rehabil Eng; 2003 Jun; 11(2):141-4. PubMed ID: 12899257 [TBL] [Abstract][Full Text] [Related]
19. [Application of semi-supervised sparse representation classifier based on help training in EEG classification]. Jia M; Wang J; Li J; Hong W Sheng Wu Yi Xue Gong Cheng Xue Za Zhi; 2014 Feb; 31(1):1-6. PubMed ID: 24804474 [TBL] [Abstract][Full Text] [Related]
20. A Sparse Representation Classification Scheme for the Recognition of Affective and Cognitive Brain Processes in Neuromarketing. Oikonomou VP; Georgiadis K; Kalaganis F; Nikolopoulos S; Kompatsiaris I Sensors (Basel); 2023 Feb; 23(5):. PubMed ID: 36904683 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]