Enhancing Autonomy and Computational Efficiency of the Self-Organising Fuzzy Neural Network for a Brain Computer Interface

DH Coyle, G Prasad, TM McGinnity

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

Abstract

This paper presents a number of enhancements to the self-organizing fuzzy neural network (SOFNN). Firstly, the SOFNN is described and a modification to the learning algorithm to improve computational efficiency is introduced. Secondly, a sensitivity analysis (SA) of the predefined SOFNN parameters is presented using electroencephalogram (EEG) data recorded from three subjects during left/right motor imagery-based brain-computer interface (BCI) experiments. This SA was carried out to determine if a general set of parameters could be used for predicting various non-stationary EEG time-series dynamics for multiple subjects. The SOFNN modifications significantly enhance computational efficiency and the SA results suggest that it may be possible to select a general set of parameters for different motor imagery-based EEG signals thus potentially enhancing the SOFNNs autonomy for application in a BCI.
Original languageEnglish
Title of host publication2006 IEEE International Conference on Fuzzy Systems
Place of PublicationUnited States
PublisherIEEE Xplore
Pages10485-10492
Number of pages8
ISBN (Print)0-7803-9489-5
DOIs
Publication statusPublished - 1 Jul 2006
Event2006 IEEE International Conference on Fuzzy Systems - Sheraton Vancouver Wall Centre Hotel, Vancouver, Canada
Duration: 16 Jul 200621 Jul 2006

Conference

Conference2006 IEEE International Conference on Fuzzy Systems
Country/TerritoryCanada
CityVancouver
Period16/07/0621/07/06

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