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Journal of Brain Research

ISSN: 2684-4583

Open Access

D.S. Aribike

Department of Chemical & Petroleum Engineering, University of Lagos, Lagos, Nigeria

Publications
  • Research   
    Wavelet Time Scattering Based Classification of Interictal and Preictal EEG Signals
    Author(s): Afreda A. Susu*, H.A. Agboola, C. Solebo, F.E.A. Lesi and D.S. Aribike

    If it were possible to reliably identify the preictal brain state from dynamical changes in EEG data of epilepsy patients, then the age long problem of actualizing a fully automated closed-loop seizure – warning or seizure-prevention system that is clinically deployable would have been resolved. Accordingly, through feature engineering, a great deal of effort has been invested over the discovery of EEG features or measures that are always indicative of the preictal brain state. However, this has proven to be difficult, time consuming and apparently unsuccessful. Therefore, lately, attention has shifted to feature learning-methods that automatically learn and extracts useful discriminatory features from raw data. This paper studies the efficacy of wavelet time scattering learned EEG features for interictal and preictal EEG classification. Wavelet time scattering ne.. Read More»
    DOI: 10.37421/2684-4583.2020.3.115

    Abstract HTML PDF

Google Scholar citation report
Citations: 2

Journal of Brain Research received 2 citations as per Google Scholar report

Journal of Brain Research peer review process verified at publons

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