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Comparisons of Signal Subspace Methods for Estimating Visual Evoked Potentials

Yusoff, Mohd Zuki and Nidal S., Kamel (2008) Comparisons of Signal Subspace Methods for Estimating Visual Evoked Potentials. In: National Postgraduate Conference on Engineering, Science and Technology (NPC 2008), March 31, 2008, Chancellors Hall, Universiti Teknologi Petronas.

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Abstract

Extracting visual evoked potentials (VEPs) from electroencephalogram (EEG) noise remains a challenging task since the signal-to-noise ratio (SNR) involved is generally very low. In this work, filtering manipulations by means of subspace approaches that break the contaminated VEP signal space into the signal subspace and the noise only subspace are introduced. Out of the two mentioned subspace, only the former is selected for further processing. Specifically, two eigendecomposition based signal subspace methods containing unique basis and estimator matrices were developed and their efficiency and performance were compared between each other. These algorithms denoted as Signal Subspace Method 1 (SSM1) and Signal Subspace Method 2 (SSM2) are able to satisfactorily extract the P100, P200 and P300 peak latencies from artificially generated noisy VEPs subjected to SNRs from 0 to -10 dB. The simulation results show that the SSM1 estimator maintains an average success rate of 87.3 %, with average errors of 5.4 for P100, 14.1 for P200 and 30.6 for P300. The SSM2 filter registers an average success rate of 93.3 %, with average errors of 9.5, 5.0 and 1.9 for P100, P200 and P300, repectively.

Item Type:Conference or Workshop Item (Speech)
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Departments / MOR / COE:Centre of Excellence > Center for Intelligent Signal and Imaging Research
ID Code:3893
Deposited By: Dr Mohd Zuki Yusoff
Deposited On:12 Jan 2011 00:34
Last Modified:19 Jan 2017 08:26

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