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Visual Evoked Potential Analysis Using Adaptive Chirplet Transform

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Advanced Biosignal Processing

Abstract

Visual evoked potentials (VEPs) are scalp electrical signals generated in response to rapid and repetitive visual stimuli. These signals possess complex time-frequency structures and are difficult to characterize with conventional methods. In this chapter, we propose a new approach based on the adaptive chirplet transform (ACT) that can represent a complete VEP response from the transient to the steady-state portion. Our implementation involves both a non-windowed and windowed approach. The non-windowed ACT employs a coarse-refinement algorithm (MPLEM) to estimate multiple chirplets under low signal-to-noise ratio condition. We show how the chirplet parameters (i.e., time-spread, chirp rate, time-center and frequency-center) can be used to separate the transient from the steady-state portions of the response, and that as few as three chirplets are required to represent a complete VEP signal. The windowed approach is implemented by partitioning the signal into equal-length non-overlapping segments before estimating a single chirplet from each segment, resulting in significant reduction of computational time. The application of the windowed ACT to VEP analysis is also discussed.

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Notes

  1. 1.

    It should be emphasized that the term “transient VEP,” or tVEP, employed here is conceptually different from that used in traditional electrophysiological literature. It usually refers to an experimental paradigm where the potentials are evoked by visual stimuli which are sufficiently widely spaced so that the visual system can be regarded as returning to a state of rest between successive stimuli [34]. In this chapter, however, tVEP refers to the signal prior to the formation of steady-state VEP.

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Authors and Affiliations

  1. School of Health Information Sciences, University of Texas Health Science Center at Houston, 7000 Fannin Street, Suite 600, Houston, TX 77054, U.S.A

    Jie Cui

  2. Department of Electrical and Computer Engineering, University of Toronto, 10 King’s College Road, Toronto, ON M5S 3G4, Canada

    Willy Wong

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  1. Jie Cui
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  2. Willy Wong
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Correspondence to Willy Wong .

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Editors and Affiliations

  1. Labo. Images, Signaux et, Systèmes Intelligents (LISSI) EA 3956, Université Paris 12, av. du Général de Gaille 61, Créteil, 94010, France

    Amine Naït-Ali

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Cui, J., Wong, W. (2009). Visual Evoked Potential Analysis Using Adaptive Chirplet Transform. In: Naït-Ali, A. (eds) Advanced Biosignal Processing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89506-0_11

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  • DOI: https://doi.org/10.1007/978-3-540-89506-0_11

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