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Complexity Measures of Event Related Potential Surface Laplacian Data Calculated Using the Wavelet Packet Transform

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Abstract

We describe a method to obtain estimates of EEG signal complexity using the well-established wavelet packet transform with best basis selection. In particular, we use the two-dimensional wavelet packet transform to obtain estimates of the complexity of two-dimensional images. This allows us to calculate complexity estimates of high-resolution brain potential maps generated from 61 scalp electrode Visual Oddball paradigm, grand-mean data. A significant reduction in the complexity of the surface Laplacian time-slices is observed during and after the Visual Potential 300 (P3) event for the target case, possibly as a result of increased spatial synchrony associated with visual-related tasks. We also present the results of a statistical analysis of the largest principal component of the time-varying complexity curves, for control, high-risk, and alcoholic groups of male subjects. Parametric and non-parametric analyses show differences in the complexity data which are significant between the control group and the alcoholic and high-risk groups.

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Authors and Affiliations

  1. Neurodynamics Laboratory, Department of Psychiatry,, SUNY Health Science Center at Brooklyn,, Brooklyn,, NY,, USA

    Kevin Jones, Henri Begleiter, Bernice Porjesz, Kongming Wang & David Chorlian

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  1. Kevin Jones
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  2. Henri Begleiter
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  3. Bernice Porjesz
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  4. Kongming Wang
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  5. David Chorlian
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Jones, K., Begleiter, H., Porjesz, B. et al. Complexity Measures of Event Related Potential Surface Laplacian Data Calculated Using the Wavelet Packet Transform. Brain Topogr 14, 333–344 (2002). https://doi.org/10.1023/A:1015708928892

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