Single-Trial Subspace-Based Approach for VEP Extraction

Kamel , Nidal and Yusoff, Mohd Zuki and Ahmad Fadzil, Mohd Hani (2010) Single-Trial Subspace-Based Approach for VEP Extraction. IEEE Transactions on Biomedical Engineering, PP (99). pp. 1-11. ISSN 0018-9294

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A signal subspace approach for extracting visual evoked potentials (VEPs) from the background electroencephalogram (EEG) colored noise without the need for a pre-whitening stage is proposed. Linear estimation of the clean signal is performed by minimizing signal distortion while maintaining the residual noise energy below some given threshold. The generalized eigendecomposition of the covariance matrices of a VEP signal and brain background EEG noise is used to transform them jointly to diagonal matrices. The generalized subspace is then decomposed into signal subspace and noise subspace. Enhancement is performed by nulling the components in the noise subspace and retaining the components in the signal subspace. The performance of the proposed algorithm is tested with simulated and real data, and compared with recently proposed signal subspace techniques. With the simulated data, the algorithms are used to estimate the latencies of P100, P200, and P300 of VEP signals corrupted by additive colored noise at different values of SNR. With the real data, the VEP signals are collected at Selayang Hospital in Kuala Lumpur, Malaysia, and the capability of the proposed algorithm in detecting the latency of P100 is obtained and compared with other subspace techniques. The ensemble averaging technique is used as a baseline for this comparison. The results indicated significant improvement by the proposed technique in terms of better accuracy and less failure rate.

Item Type: Article
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Departments / MOR / COE: Centre of Excellence > Center for Intelligent Signal and Imaging Research
Depositing User: Dr Mohd Zuki Yusoff
Date Deposited: 17 Jan 2011 00:36
Last Modified: 19 Mar 2014 03:58

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