A rule based approach to classification of EEG datasets: a comparison between ANFIS and rough sets

Jahankhani, Pari and Revett, Kenneth and Kodogiannis, Vassilis (2008) A rule based approach to classification of EEG datasets: a comparison between ANFIS and rough sets. In: Proceedings of the Ninth Symposium on Neural Network Applications in Electrical Engineering: NEUREL 2008, September 27-28, 2008, Faculty of Electrical Engineering, University of Belgrade. IEEE, Los Alamitos, USA, pp. 157-160. ISBN 9781424429035

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Official URL: http://dx.doi.org/10.1109/NEUREL.2008.4685599

Abstract

This paper compares two different rule based classification methods in order to evaluate their relative efficiacy with respect to classification accuracy and the caliber of the resulting rules. Specifically, the application of Adaptive Neuro-Fuzzy Inference System (ANFIS) and rough sets were deployed on a complete dataset consisting of electroencephalogram (EEG) data. The results indicate that both were able to classify this dataset accurately and the number of rules were similar in both cases, provided the dataset was pre-processed using PCA in the case of ANFIS.

Item Type: Book Section
Uncontrolled Keywords: Neuro-fuzzy systems, PCA, Rough sets, electroencephalography, wavelets
Subjects: University of Westminster > Science and Technology > Electronics and Computer Science, School of (No longer in use)
Depositing User: Miss Nina Watts
Date Deposited: 19 Dec 2008 13:14
Last Modified: 11 Aug 2010 14:34
URI: http://westminsterresearch.wmin.ac.uk/id/eprint/5634

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