Title
Evolutionary fuzzy ARTMAP for autoregressive model order selection and classification of EEG signals
Abstract
A new technique of fusing genetic algorithms with Fuzzy ARTMAP is proposed. This method selects the appropriate autoregressive model order for EEG signals and consequently classifies these signals into their respective different mental tasks. The experimental results show that this method outperforms other statistical autoregressive model order selection methods like Akaike Information Criterion, Final Prediction Error and reflection coefficient
Year
DOI
Venue
2000
10.1109/ICSMC.2000.886582
SMC
Keywords
Field
DocType
art neural nets,electroencephalography,evolutionary computation,fuzzy neural nets,genetic algorithms,medical signal processing,signal classification,eeg signals,autoregressive model,fuzzy artmap,order selection,error correction,genetic engineering,akaike information criterion,genetic algorithm,reflection,reflection coefficient,predictive models,regression analysis,parameter estimation
Autoregressive model,Mean squared prediction error,Akaike information criterion,Pattern recognition,Computer science,Fuzzy logic,Evolutionary computation,Signal classification,Artificial intelligence,Electroencephalography,Machine learning,Genetic algorithm
Conference
Volume
ISSN
ISBN
5
1062-922X
0-7803-6583-6
Citations 
PageRank 
References 
6
1.28
2
Authors
4
Name
Order
Citations
PageRank
Palaniappan, R.1316.13
Paramesran Raveendran218320.02
Shogo Nishida322752.41
Saiwaki, N.481.94