Wearable EEG Neurofeedback Based-on Machine Learning Algorithms for Children with Autism: A Randomized, Placebo-controlled Study.

Journal: Current medical science
PMID: 39565505

Abstract

OBJECTIVE: Behavioral interventions have been shown to ameliorate the electroencephalogram (EEG) dynamics underlying the behavioral symptoms of autism spectrum disorder (ASD), while studies have also demonstrated that mirror neuron mu rhythm-based EEG neurofeedback training improves the behavioral functioning of individuals with ASD. This study aimed to test the effects of a wearable mu rhythm neurofeedback training system based on machine learning algorithms for children with autism.

Authors

  • Xian-Na Wang
    Capital Medical University School of Rehabilitation Medicine, Beijing, 100068, China.
  • Tong Zhang
    Beijing University of Chinese Medicine, Beijing, China.
  • Bi-Cheng Han
    Qiangnao Keji (BrainCo) Ltd., Hangzhou, 310027, China.
  • Wei-Wei Luo
    Capital Medical University School of Rehabilitation Medicine, Beijing, 100068, China.
  • Wen-Hui Liu
    Capital Medical University School of Rehabilitation Medicine, Beijing, 100068, China.
  • Zhao-Yi Yang
    Qiangnao Keji (BrainCo) Ltd., Hangzhou, 310027, China.
  • A Disi
    BrainCo., Inc., Somerville, Massachusetts, 02143, USA.
  • Yue Sun
    Department of Rheumatology, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, China.
  • Jin-Chen Yang
    Qiangnao Keji (BrainCo) Ltd., Hangzhou, 310027, China. jinchen.yang@brainco.tech.

Keywords

External Resources

Popular Topics
Recent Journals