A preliminary investigation of the deconvolution of auditory evoked potentials using a session jittering paradigm

Tao Wang, Changan Zhan, Gang Yan, Jorge Bohorquez, Ozcan Ozdamar

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

Objective. In the high stimulus rate studies of auditory evoked potentials (AEPs), deconvolution techniques have been developed to unwrap the overlapped responses based on the jittering strategy of stimulus onset asynchronies (SOAs). This study investigates an alternative deconvolution method (multi-rate steady-state averaging deconvolution, MSAD) using a session-jittering strategy where steady-state responses recorded at different SOAs can be adequate to derive the transient-AEP. Approach. A linear transform model was developed to solve the deconvolution problem, and the mathematical properties of the transform matrix were explored by singular value decomposition, which indicates the need for regularization techniques to solve the ill-conditioning of the matrix. Main results. The performance evaluated by both synthetic and experimental data is satisfactory compared with the classic SOA-jittering method commonly known as the continuous loop averaging deconvolution. Significance. Our initial investigations suggest that the MSAD method is promising in terms of SOA insensitivity, sequence robustness and recording flexibility. However, more evaluation is needed to make the method suitable for a general application of the high stimulus rate paradigm.

Original languageEnglish
Article number026023
JournalJournal of Neural Engineering
Volume10
Issue number2
DOIs
StatePublished - Apr 1 2013

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Auditory Evoked Potentials
Bioelectric potentials
Deconvolution
Linear Models
Singular value decomposition

ASJC Scopus subject areas

  • Biomedical Engineering
  • Cellular and Molecular Neuroscience

Cite this

A preliminary investigation of the deconvolution of auditory evoked potentials using a session jittering paradigm. / Wang, Tao; Zhan, Changan; Yan, Gang; Bohorquez, Jorge; Ozdamar, Ozcan.

In: Journal of Neural Engineering, Vol. 10, No. 2, 026023, 01.04.2013.

Research output: Contribution to journalArticle

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