Extracting Features for a Brain-Computer Interface by Self-Organizing Fuzzy Neural Network-based Time Series Prediction

DH Coyle; G Prasad; TM McGinnity

doi:10.1109/IEMBS.2004.1404216

Extracting Features for a Brain-Computer Interface by Self-Organizing Fuzzy Neural Network-based Time Series Prediction

DH Coyle, G Prasad, TM McGinnity

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

Abstract

This paper presents a novel feature extraction procedure (FEP) for extracting features from the electroencephalogram (EEG) recorded from subjects producing right and left motor imagery. Four self-organizing fuzzy neural networks (SOFNNs) are coalesced to perform one-step-ahead predictions for the EEG time series data. Features are derived from the mean squared error (MSE) in prediction or the mean squared of the predicted signals (MSY). Classification is performed using linear discriminant analysis (LDA). This novel FEP is tested on three subjects offline and classification accuracy (CA) rates approach 94% with information transfer (IT) rates >10 bits/min. Minimum subject specific data analysis is required and the approach shows good potential for online feature extraction and autonomous system adaptation.

Original language	English
Title of host publication	The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Place of Publication	United States
Publisher	IEEE Xplore
Pages	4371-4374
Number of pages	4
DOIs	https://doi.org/10.1109/IEMBS.2004.1404216
Publication status	Published - 1 Sept 2004
Event	Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Duration: 1 Sept 2004 → 5 Sept 2004 Conference number: 26

Conference

Conference	Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Abbreviated title	EMBC
Period	1/09/04 → 5/09/04

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10.1109/IEMBS.2004.1404216

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Extracting Features for a Brain-Computer Interface by Self-Organizing Fuzzy Neural Network-based Time Series Prediction. / Coyle, DH; Prasad, G; McGinnity, TM.
The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. United States: IEEE Xplore, 2004. p. 4371-4374.

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

Coyle, DH, Prasad, G & McGinnity, TM 2004, Extracting Features for a Brain-Computer Interface by Self-Organizing Fuzzy Neural Network-based Time Series Prediction. in The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Xplore, United States, pp. 4371-4374, Annual International Conference of the IEEE Engineering in Medicine and Biology Society , 1/09/04. https://doi.org/10.1109/IEMBS.2004.1404216

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title = "Extracting Features for a Brain-Computer Interface by Self-Organizing Fuzzy Neural Network-based Time Series Prediction",

abstract = "This paper presents a novel feature extraction procedure (FEP) for extracting features from the electroencephalogram (EEG) recorded from subjects producing right and left motor imagery. Four self-organizing fuzzy neural networks (SOFNNs) are coalesced to perform one-step-ahead predictions for the EEG time series data. Features are derived from the mean squared error (MSE) in prediction or the mean squared of the predicted signals (MSY). Classification is performed using linear discriminant analysis (LDA). This novel FEP is tested on three subjects offline and classification accuracy (CA) rates approach 94% with information transfer (IT) rates >10 bits/min. Minimum subject specific data analysis is required and the approach shows good potential for online feature extraction and autonomous system adaptation.",

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AB - This paper presents a novel feature extraction procedure (FEP) for extracting features from the electroencephalogram (EEG) recorded from subjects producing right and left motor imagery. Four self-organizing fuzzy neural networks (SOFNNs) are coalesced to perform one-step-ahead predictions for the EEG time series data. Features are derived from the mean squared error (MSE) in prediction or the mean squared of the predicted signals (MSY). Classification is performed using linear discriminant analysis (LDA). This novel FEP is tested on three subjects offline and classification accuracy (CA) rates approach 94% with information transfer (IT) rates >10 bits/min. Minimum subject specific data analysis is required and the approach shows good potential for online feature extraction and autonomous system adaptation.

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M3 - Chapter in a published conference proceeding

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BT - The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

PB - IEEE Xplore

CY - United States

T2 - Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Y2 - 1 September 2004 through 5 September 2004

ER -

Extracting Features for a Brain-Computer Interface by Self-Organizing Fuzzy Neural Network-based Time Series Prediction

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