title: Adapted filter banks for feature extraction in transcranial magnetic stimulation evoked responses.
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.
authors.affiliation: Computational Diagnostics and Biocybernetics Unit, Saarland University Hospital and Saarland University of Applied Sciences, Homburg/Saar, Germany.
authors.forename: Arief R
authors.initials: AR
authors.lastname: Harris
authors.forename: Karsten
authors.initials: K
authors.lastname: Schwerdtfeger
authors.forename: Daniel J
authors.initials: DJ
authors.lastname: Strauss
mesh_terms: D000328:Adult; D000465:Algorithms; D001921:Brain / Q000502:physiology*; D004569:Electroencephalography / Q000379:methods; D005071:Evoked Potentials / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D012815:Signal Processing, Computer-Assisted; D050781:Transcranial Magnetic Stimulation / Q000379:methods*; D055815:Young Adult