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Faster Self-Organizing Fuzzy Neural Network Training and a Hyperparameter Analysis for a Brain-Computer Interface

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Coyle, Damien, Prasad, Girijesh and McGinnity, Martin (2009) Faster Self-Organizing Fuzzy Neural Network Training and a Hyperparameter Analysis for a Brain-Computer Interface. IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS, 39 (6). pp. 1458-1471. [Journal article]

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URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5032132&isnumber=5238715

DOI: 10.1109/TSMCB.2009.2018469

Abstract

This paper introduces a number of modifications to the learning algorithm of the self-organizing fuzzy neural network (SOFNN) to improve computational efficiency. It is shown that the modified SOFNN compares favorably to other evolving fuzzy systems in terms of accuracy and structural complexity. An analysis of the SOFNNs effectiveness when applied in an electroencephalogram (EEG)-based brain-computer interface (BCI) involving the neural-time-series-prediction-preprocessing (NTSPP) framework is also presented, where a sensitivity analysis (SA) of the SOFNN hyperparameters was performed using EEG data recorded from three subjects during left/right motor imagery-based BCI experiments. The aim of this one-time SA was to eliminate the need to choose subject- and signal-specific hyperparameters for the SOFNN and thus apply the SOFNN in the NTSPP framework as a parameterless self-organizing framework for EEG preprocessing. The results indicate that a general set of NTSPP parameters chosen via the SA provide the best results when tested in a BCI system. Therefore, with this general set of SOFNN parameters and its self-organizing structure, in conjunction with parameterless feature extraction and linear discriminant classification, a fully parameterless BCI which lends itself well to autonomous adaptation is realizable.

Item Type:Journal article
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute > Intelligent Systems Research Centre
ID Code:4140
Deposited By:Dr Damien Coyle
Deposited On:04 Jan 2010 14:21
Last Modified:23 Jun 2011 11:02

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