An efficient deep learning framework for P300 evoked related potential detection in EEG signal.

Journal: Computer methods and programs in biomedicine
Published Date: Dec 25, 2022

Abstract

BACKGROUND: Incorporating the time-frequency localization properties of Gabor transform (GT), the complexity understandings of convolutional neural network (CNN), and histogram of oriented gradients (HOG) efficacy in distinguishing positive peaks can exhibit their characteristics to reveal an effective solution in the detection of P300 evoked related potential (ERP). By applying a drastic number of convolutional layers, the majority of deep networks elicit sufficient properties for the output determination, leading to gigantic and time-consuming structures. In this paper, we propose a novel deep learning framework by the combination of tuned GT, and modified HOG with the CNN as "TGT-MHOG-CNN" for detection of P300 ERP in EEG signal.

Authors

  • Pedram Havaei
    Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran; Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran. Electronic address: p.havaei@alumni.iut.ac.ir.
  • Maryam Zekri
    Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran.
  • Elham Mahmoudzadeh
    Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran. Electronic address: mahmoudzadeh@iut.ac.ir.
  • Hossein Rabbani

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