Use of deep learning to detect personalized spatial-frequency abnormalities in EEGs of children with ADHD.

Journal: Journal of neural engineering
Published Date: Nov 19, 2019

Abstract

OBJECTIVE: Attention-deficit/hyperactivity disorder (ADHD) is one of the most prevalent neurobehavioral disorders. Studies have tried to find the neural correlations of ADHD with electroencephalography (EEG). Due to the heterogeneity in the ADHD population, a multivariate EEG profile is useful, and the detection of a personalized abnormality in EEG is urgently needed. Deep learning algorithms, especially convolutional neural network (CNN), have made tremendous progress recently, and are expected to solve the problem.

Authors

  • He Chen
    School of Food and Biological Engineering, Shaanxi University of Science and Technology Xi&#;an, China.
  • Yan Song
    Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, China.
  • Xiaoli Li
    State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.

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