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BioMed Research International - Volume 2016 2016, Article ID 8569129, 9 pages -

Research Article

School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou 221116, China

Department of Internal Neurology, Xuzhou Central Hospital, Xuzhou 221116, China

Received 7 June 2016; Revised 25 August 2016; Accepted 14 September 2016

Academic Editor: Tun-Wen Pai

Copyright © 2016 Nannan Yu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Sparse representation is a powerful tool in signal denoising, and visual evoked potentials VEPs have been proven to have strong sparsity over an appropriate dictionary. Inspired by this idea, we present in this paper a novel sparse representation-based approach to solving the VEP extraction problem. The extraction process is performed in three stages. First, instead of using the mixed signals containing the electroencephalogram EEG and VEPs, we utilise an EEG from a previous trial, which did not contain VEPs, to identify the parameters of the EEG autoregressive AR model. Second, instead of the moving average MA model, sparse representation is used to model the VEPs in the autoregressive-moving average ARMA model. Finally, we calculate the sparse coefficients and derive VEPs by using the AR model. Next, we tested the performance of the proposed algorithm with synthetic and real data, after which we compared the results with that of an AR model with exogenous input modelling and a mixed overcomplete dictionary-based sparse component decomposition method. Utilising the synthetic data, the algorithms are then employed to estimate the latencies of P100 of the VEPs corrupted by added simulated EEG at different signal-to-noise ratio SNR values. The validations demonstrate that our method can well preserve the details of the VEPs for latency estimation, even in low SNR environments.





Autor: Nannan Yu, Funian Hu, Dexuan Zou, Qisheng Ding, and Hanbing Lu

Fuente: https://www.hindawi.com/



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