Data augmentation for deep-learning-based electroencephalography.

Journal: Journal of neuroscience methods
Published Date: Jul 31, 2020

Abstract

BACKGROUND: Data augmentation (DA) has recently been demonstrated to achieve considerable performance gains for deep learning (DL)-increased accuracy and stability and reduced overfitting. Some electroencephalography (EEG) tasks suffer from low samples-to-features ratio, severely reducing DL effectiveness. DA with DL thus holds transformative promise for EEG processing, possibly like DL revolutionized computer vision, etc. NEW METHOD: We review trends and approaches to DA for DL in EEG to address: Which DA approaches exist and are common for which EEG tasks? What input features are used? And, what kind of accuracy gain can be expected?

Authors

  • Elnaz Lashgari
    Schmid College of Science and Technology, Chapman University, United States; Institute for Interdisciplinary Brain and Behavioral Sciences, Chapman University, United States. Electronic address: Lashgari@chapman.edu.
  • Dehua Liang
    Schmid College of Science and Technology, Chapman University, United States; Institute for Interdisciplinary Brain and Behavioral Sciences, Chapman University, United States.
  • Uri Maoz
    Computation and Neural Systems, California Institute of Technology, Pasadena, CA 91125, USA; Computational Neuroscience, Health and Behavioral Sciences and Brain Institute, Chapman University, Orange, CA 92866, USA; Institute for Interdisciplinary Brain and Behavioral Sciences, Chapman University, Orange, CA 92866, USA; Department of Anesthesiology, School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA.

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