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Applying deep learning in brain computer interface to classify motor imagery.

, , and . J. Intell. Fuzzy Syst., 45 (5): 8747-8760 (November 2023)

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Control Loop Performance Assessment With a Dynamic Neuro-Fuzzy Model (dFasArt)., , and . IEEE Trans Autom. Sci. Eng., 9 (2): 377-389 (2012)How well Fuzzy ARTMAP approximates functions?, , , , and . J. Intell. Fuzzy Syst., 25 (2): 335-350 (2013)dNSP: A biologically inspired dynamic Neural network approach to Signal Processing., , , and . Neural Networks, 21 (7): 1006-1019 (2008)Feature Selection Applying Statistical and Neurofuzzy Methods to EEG-Based BCI., , and . Comput. Intell. Neurosci., (2015)Applying deep learning in brain computer interface to classify motor imagery., , and . J. Intell. Fuzzy Syst., 45 (5): 8747-8760 (November 2023)Performance Monitoring of Closed-Loop Controlled Systems Using dFasArt., , and . IWINAC (2), volume 4528 of Lecture Notes in Computer Science, page 45-53. Springer, (2007)Are low cost Brain Computer Interface headsets ready for motor imagery applications?, , and . Expert Syst. Appl., (2016)Voting Strategy to Enhance Multimodel EEG-Based Classifier Systems for Motor Imagery BCI., , and . IEEE Syst. J., 10 (3): 1082-1088 (2016)Improvement of the neighborhood based Levenberg-Marquardt algorithm by local adaptation of the learning coefficient., , , and . IEEE Trans. Neural Networks, 16 (4): 988-992 (2005)dFasArt: Dynamic neural processing in FasArt model., , , and . Neural Networks, 22 (4): 479-487 (2009)