Faster Self-Organizing Fuzzy Neural Network Training and a Hyperparameter Analysis for a Brain–Computer Interface
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T. Martin McGinnity | Girijesh Prasad | Damien Coyle | G. Prasad | D. Coyle | T. McGinnity | T. Mcginnity
[1] J. S. Barlow,et al. Changes in EEG mean frequency and spectral purity during spontaneous alpha blocking. , 1990, Electroencephalography and clinical neurophysiology.
[2] Karla Felix Navarro,et al. A Comprehensive Survey of Brain Interface Technology Designs , 2007, Annals of Biomedical Engineering.
[3] Mauro Birattari,et al. Lazy Learning Meets the Recursive Least Squares Algorithm , 1998, NIPS.
[4] G Pfurtscheller,et al. Estimating the Mutual Information of an EEG-based Brain-Computer Interface , 2002, Biomedizinische Technik. Biomedical engineering.
[5] Rabab K Ward,et al. A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals , 2007, Journal of neural engineering.
[6] T. Martin McGinnity,et al. An on-line algorithm for creating self-organizing fuzzy neural networks , 2004, Neural Networks.
[7] Girijesh Prasad,et al. Neural time-series prediction preprocessing meets common spatial patterns in a brain-computer interface , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[8] William Z Rymer,et al. Guest Editorial Brain–Computer Interface Technology: A Review of the Second International Meeting , 2001 .
[9] B. Everitt,et al. Applied Multivariate Data Analysis. , 1993 .
[10] Simon Parsons,et al. Soft computing: fuzzy logic, neural networks and distributed artificial intelligence by F. Aminzadeh and M. Jamshidi (Eds.), PTR Prentice Hall, Englewood Cliffs, NJ, pp 301, ISBN 0-13-146234-2 , 1996, Knowl. Eng. Rev..
[11] Nikola K. Kasabov,et al. DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction , 2002, IEEE Trans. Fuzzy Syst..
[12] Girijesh Prasad,et al. Improving information transfer rates of a Brain-Computer Interface by self-organising fuzzy neural network-based Multi-step ahead time-series prediction , 2004 .
[13] Gang Leng. Algorithmic developments for self-organising fuzzy neural networks , 2004 .
[14] Babak Hassibi,et al. Second Order Derivatives for Network Pruning: Optimal Brain Surgeon , 1992, NIPS.
[15] Nikola Kasabov,et al. Evolving Connectionist Systems: Methods and Applications in Bioinformatics, Brain Study and Intelligent Machines , 2002, IEEE Transactions on Neural Networks.
[16] Edwin Lughofer,et al. FLEXFIS: A Variant for Incremental Learning of Takagi-Sugeno Fuzzy Systems , 2005, The 14th IEEE International Conference on Fuzzy Systems, 2005. FUZZ '05..
[17] B. Hjorth. EEG analysis based on time domain properties. , 1970, Electroencephalography and clinical neurophysiology.
[18] Girijesh Prasad,et al. Improving signal separability and inter-session stability of a motor imagery-based brain-computer interface by neural-time-series-prediction-preprocessing , 2005 .
[19] M. Sugeno,et al. Derivation of Fuzzy Control Rules from Human Operator's Control Actions , 1983 .
[20] Damien Hugh Coyle. Intelligent preprocessing and feature extraction techniques for a brain-computer interface , 2006 .
[21] Klaus-Robert Müller,et al. The non-invasive Berlin Brain–Computer Interface: Fast acquisition of effective performance in untrained subjects , 2007, NeuroImage.
[22] Antonello Rizzi,et al. Adaptive resolution min-max classifiers , 2002, IEEE Trans. Neural Networks.
[23] G Pfurtscheller,et al. Separability of EEG signals recorded during right and left motor imagery using adaptive autoregressive parameters. , 1998, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.
[24] T.M. McGinnity,et al. A time-series prediction approach for feature extraction in a brain-computer interface , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[25] D.P. Filev,et al. An approach to online identification of Takagi-Sugeno fuzzy models , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[26] Bruce J. Fisch,et al. Fisch and Spehlmann's Eeg Primer: Basic Principles of Digital and Analog Eeg , 1999 .
[27] G Pfurtscheller,et al. EEG-based communication: improved accuracy by response verification. , 1998, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.
[28] Nikola K. Kasabov,et al. Evolving fuzzy neural networks for supervised/unsupervised online knowledge-based learning , 2001, IEEE Trans. Syst. Man Cybern. Part B.
[29] Meng Joo Er,et al. Dynamic fuzzy neural networks-a novel approach to function approximation , 2000, IEEE Trans. Syst. Man Cybern. Part B.
[30] Narasimhan Sundararajan,et al. An efficient sequential learning algorithm for growing and pruning RBF (GAP-RBF) networks , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[31] G. Williams. Chaos theory tamed , 1997 .
[32] G. Pfurtscheller,et al. Brain-Computer Interfaces for Communication and Control. , 2011, Communications of the ACM.
[33] T. Martin McGinnity,et al. A multi-class brain-computer interface with SOFNN-based prediction preprocessing , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).