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 mirr...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.Methods:A randomized,placebo-controlled study was carried out on 60 participants aged 3 to 6 years who were diagnosed with autism,at two center-based intervention sites.The neurofeedback group received active mu rhythm neurofeedback training,while the control group received a sham neurofeedback training.Other behavioral intervention programs were similar between the two groups.Results:After 60 sessions of treatment,both groups showed significant improvements in several domains including language,social and problem behavior.The neurofeedback group showed significantly greater improvements in expressive language(P=0.013)and cognitive awareness(including joint attention,P=0.003)than did the placebo-controlled group.Conclusion:Artificial intelligence-powered wearable EEG neurofeedback,as a type of brain-computer interface application,is a promising assistive technology that can provide targeted intervention for the core brain mechanisms underlying ASD symptoms.展开更多
基金funded by a grant from Qiangnao Keji(BrainCo)Ltd.
文摘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.Methods:A randomized,placebo-controlled study was carried out on 60 participants aged 3 to 6 years who were diagnosed with autism,at two center-based intervention sites.The neurofeedback group received active mu rhythm neurofeedback training,while the control group received a sham neurofeedback training.Other behavioral intervention programs were similar between the two groups.Results:After 60 sessions of treatment,both groups showed significant improvements in several domains including language,social and problem behavior.The neurofeedback group showed significantly greater improvements in expressive language(P=0.013)and cognitive awareness(including joint attention,P=0.003)than did the placebo-controlled group.Conclusion:Artificial intelligence-powered wearable EEG neurofeedback,as a type of brain-computer interface application,is a promising assistive technology that can provide targeted intervention for the core brain mechanisms underlying ASD symptoms.
