Single-Trial Sparse Representation-Based Approach for VEP Extraction.

Nannan Yu,Hanbing Lu,Dexuan Zou,Funian Hu,Qisheng Ding

doi:10.1155/2016/8569129

Nannan Yu, Hanbing Lu + Show 3 more

Open Access

https://doi.org/10.1155/2016/8569129

Copy DOI

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.

Highlights

Evoked potentials (EPs) are bioelectrical signals that are generated by the central nervous system when the latter is stimulated by well-defined external stimuli
Computer simulation is conducted to verify the performance of our proposed visual evoked potentials (VEPs) signal extraction method
The background EEG that is superimposed on the EP signal is simulated by an AR process [18], which is given by e (k) = 1.5084e (k − 1) − 0.1587e (k − 2) (16)

Summary

Introduction

Evoked potentials (EPs) are bioelectrical signals that are generated by the central nervous system when the latter is stimulated by well-defined external stimuli. Depending on the modality of stimulation, EPs are categorised into auditory evoked potential (AEP), visual evoked potential (VEP), and somatosensory evoked potential (SEP). In clinical environments, these signals are used to reflect the various functions of auditory, optic, and sensory nerve sense-conducting pathways. We concentrate on the second type, namely, the VEPs. Generally speaking, there exist three prominent components (N75, P100, and N145) in the VEP signal, whereas the preceding and following segments are almost flat. The P100 wave is the most significant and stable; it is the most important component in clinical applications [1]

Objectives

Methods

Results

Conclusion