Estimation of Visual Evoked Potentials using a Signal Subspace Approach

Kamel , Nidal (2007) Estimation of Visual Evoked Potentials using a Signal Subspace Approach. International Conference on Intelligent and Advanced Systems 2007. pp. 1157-1161.

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Extraction of visual evoked potentials (VEPs) from the human brain is generally very difficult due to its poor signal-to-noise ratio (SNR) property. A signal subspace technique is presented to estimate VEPs hidden inside highly colored electroencephalogram (EEG) noise. This method is borrowed and modified from signal subspace techniques originally used for enhancing speech corrupted by colored noise. The signal subspace is estimated by applying eigenvalue decomposition on the approximated signal covariance matrix. The signal subspacebased algorithm is able to satisfactorily extract the P100, P200 and P300 peak latencies from artificially generated noisy VEPs. The simulation results show that the estimator maintains an average success rate of 87 % with an average percentage error of less than 9 %, when subjected to SNR from 0 dB to -10 dB.

Item Type: Article
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Departments / MOR / COE: Research Institutes > Institute for Health Analytics
Depositing User: Assoc Prof Dr Nidal Kamel
Date Deposited: 25 Apr 2011 06:55
Last Modified: 19 Jan 2017 08:26

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