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   -> Volume 2, Issue 16

Preprint available: Application of wavelets to EEG
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Author Message (Leonard J. Trejo)

PostPosted: Fri Nov 29, 2002 3:38 pm    
Subject: Preprint available: Application of wavelets to EEG
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Preprint available: Application of wavelets to EEG

Preprint available -- Application of wavelets to feature extraction of EEG

(Request pre-print by e-mail message to

Trejo, L. J. and Shensa, M. J. (1993b, in press). Linear and neural
network models for predicting human signal detection performance from
event-related potentials: A comparison of the wavelet transform with
other feature extraction methods. Proceedings of the 1993 Simulation
Technology Multiconference, San Francisco, CA, November 7-10. San
Diego: Society for Computer Simulation.


This report describes the development and evaluation of mathematical
models for predicting human performance from discrete wavelet
transforms (DWT) of event-related potentials (ERP) elicited by
task-relevant stimuli. The DWT was compared to principal components
analysis (PCA) for representation of ERPs in linear regression
and neural network models developed to predict a composite measure
of human signal detection performance. Linear regression models
based on coefficients of the decimated DWT predicted signal
detection performance with half as many free parameters as
comparable models based on PCA scores and were relatively more
resistant to model degradation due to over-fitting.

Feed-forward neural networks were trained using the backpropagation
algorithm to predict signal detection performance based on raw ERPs,
PCA scores, or high-power coefficients of the the DWT. Neural
networks based on high-power DWT coefficients trained with fewer
iterations, generalized to new data better, and were more resistant
to overfitting than networks based on raw ERPs. Networks based on
PCA scores did not generalize to new data as well as either the DWT
network or the raw ERP network.

The results show that wavelet expansions represent the ERP
efficiently and extract behaviorally important features for use in
linear regression or neural network models of human performance.
The efficiency of the DWT is discussed in terms of its decorrelation
and energy compaction properties. In addition, the DWT models
provided evidence that a pattern of low-frequency activity (1 to 3.5
Hz) occurring at specific times and scalp locations is a reliable
correlate of human signal detection performance.
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