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Authors: Conor McCooey and Dinesh Kant Kumar

Affiliation: School of Electrical & Computer Engineering, RMIT University, Australia

Abstract: A new method of viewing evoked potential data is described. This method, called the peak detection method, is based on singularity detection using the discrete wavelet transform. The peaks and troughs of an EEG recording are identified and characterized using the algorithms of this method, resulting in a linear decomposition of the data into sets of individual peaks. Then, by classifying the peaks in terms of latency (time), magnitude (voltage potential) and width (scale), the locations of higher concentrations of similar peaks are identified. These are grouped and sub-averaged, yielding sets of sub-averaged peaks that characterize the shape and give a measure of the repeatability of particular sub-averages within the visual evoked potential ensemble average signal. This method highlights evoked potential features that may be obscured or cancelled out with standard ensemble averaging. This is demonstrated for visual evoked potential data taken from a single subject.

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Paper citation in several formats:
McCooey, C. and Kant Kumar, D. (2005). Characterising Evoked Potential Signals Using Wavelet Transform Singularity Detection. In Proceedings of the 1st International Workshop on Biosignal Processing and Classification (ICINCO 2005) - BPC; ISBN 972-8865-35-X, SciTePress, pages 3-11. DOI: 10.5220/0001191700030011

@conference{bpc05,
author={Conor McCooey and Dinesh {Kant Kumar}},
title={Characterising Evoked Potential Signals Using Wavelet Transform Singularity Detection},
booktitle={Proceedings of the 1st International Workshop on Biosignal Processing and Classification (ICINCO 2005) - BPC},
year={2005},
pages={3-11},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001191700030011},
isbn={972-8865-35-X},
}

TY - CONF

JO - Proceedings of the 1st International Workshop on Biosignal Processing and Classification (ICINCO 2005) - BPC
TI - Characterising Evoked Potential Signals Using Wavelet Transform Singularity Detection
SN - 972-8865-35-X
AU - McCooey, C.
AU - Kant Kumar, D.
PY - 2005
SP - 3
EP - 11
DO - 10.5220/0001191700030011
PB - SciTePress

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