Single-Trial Sparse Representation-Based Approach for VEP Extraction
Joint Authors
Yu, Nannan
Hu, Funian
Zou, Dexuan
Ding, Qisheng
Lu, Hanbing
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-10-11
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Yu, Nannan& Hu, Funian& Zou, Dexuan& Ding, Qisheng& Lu, Hanbing. 2016. Single-Trial Sparse Representation-Based Approach for VEP Extraction. BioMed Research International،Vol. 2016, no. 2016, pp.1-9.
https://search.emarefa.net/detail/BIM-1099090
Modern Language Association (MLA)
Yu, Nannan…[et al.]. Single-Trial Sparse Representation-Based Approach for VEP Extraction. BioMed Research International No. 2016 (2016), pp.1-9.
https://search.emarefa.net/detail/BIM-1099090
American Medical Association (AMA)
Yu, Nannan& Hu, Funian& Zou, Dexuan& Ding, Qisheng& Lu, Hanbing. Single-Trial Sparse Representation-Based Approach for VEP Extraction. BioMed Research International. 2016. Vol. 2016, no. 2016, pp.1-9.
https://search.emarefa.net/detail/BIM-1099090
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1099090