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Adapted filter banks for feature extraction in transcranial magnetic stimulation evoked responses

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Abstract

A novel adaptive and approximate shift-invariant wavelet packet feature extraction scheme for event-related potentials (ERPs) in the electroencephalogram (EEG) is introduced in this paper. In this algorithm, the shift-invariant wavelet packed decomposition is done by integrating a cost function for decimation decision in each sub-band expansion. Additionally, a shape adaptation of the wavelet is implemented to find the best adapted wavelet shape for a given class of ERPs. This scheme is used to analyze the time course of the impact of single-pulse transcranial magnetic stimulation (TMS) to the auditory ERPs. We show that the proposed scheme is able to extract even slightest impacts of TMS, making it a promising tool for the extraction of weak ERPs components, particularly in hybrid TMS–EEG/ERP setups.

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Authors and Affiliations

  1. Computational Diagnostics and Biocybernetics Unit, Saarland University Hospital and Saarland University of Applied Sciences, Homburg/Saar, Germany

    Arief R. Harris & Daniel J. Strauss

  2. Department of Neurosurgery, Saarland University Hospital, Homburg/Saar, Germany

    Karsten Schwerdtfeger

  3. Leibniz-Institute for New Materials and Key Numerics, Saarbrücken, Germany

    Daniel J. Strauss

Authors
  1. Arief R. Harris
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  2. Karsten Schwerdtfeger
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  3. Daniel J. Strauss
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Correspondence to Daniel J. Strauss.

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Harris, A.R., Schwerdtfeger, K. & Strauss, D.J. Adapted filter banks for feature extraction in transcranial magnetic stimulation evoked responses. Med Biol Eng Comput 49, 221–231 (2011). https://doi.org/10.1007/s11517-010-0726-7

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  • DOI: https://doi.org/10.1007/s11517-010-0726-7

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