A Generative Model to Synthesize EEG Data for Epileptic Seizure Prediction.

Journal: IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Published Date: Nov 10, 2021

Abstract

OBJECTIVE: Scarcity of good quality electroencephalography (EEG) data is one of the roadblocks for accurate seizure prediction. This work proposes a deep convolutional generative adversarial network (DCGAN) to generate synthetic EEG data. Another objective of our study is to use transfer-learning (TL) for evaluating the performance of four well-known deep-learning (DL) models to predict epileptic seizure.

Authors

  • Khansa Rasheed
  • Junaid Qadir
    Department of Computer Engineering, Qatar University, Doha, Qatar.
  • Terence J O'Brien
  • Levin Kuhlmann
    4 Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, Victoria, 3010, Australia.
  • Adeel Razi

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