Introducing Region Based Pooling for handling a varied number of EEG channels for deep learning models.

Journal: Frontiers in neuroinformatics
Published Date: Jan 30, 2024

Abstract

INTRODUCTION: A challenge when applying an artificial intelligence (AI) deep learning (DL) approach to novel electroencephalography (EEG) data, is the DL architecture's lack of adaptability to changing numbers of EEG channels. That is, the number of channels cannot vary neither in the training data, nor upon deployment. Such highly specific hardware constraints put major limitations on the clinical usability and scalability of the DL models.

Authors

  • Thomas Tveitstøl
    Department of Neurology, Oslo University Hospital, Oslo, Norway.
  • Mats Tveter
    Department of Neurology, Oslo University Hospital, Oslo, Norway.
  • Ana S Pérez T
    Department of Neurology, Oslo University Hospital, Oslo, Norway.
  • Christoffer Hatlestad-Hall
    Department of Neurology, Oslo University Hospital, Oslo, Norway.
  • Anis Yazidi
    Department of Computer Science, Oslo Metropolitan University, Oslo, Norway.
  • Hugo L Hammer
    Department of Computer Science, Oslo Metropolitan University, Oslo, Norway.
  • Ira R J Hebold Haraldsen
    Department of Neurology, Oslo University Hospital, Oslo, Norway.

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