Trial reportCurrent medical science2024
Wearable EEG Neurofeedback Based-on Machine Learning Algorithms for Children with Autism: A Randomized, Placebo-controlled Study.
Trial report in Current medical science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Multimodal Human-Computer Interaction Interventions for Children and Adolescents With Autism Spectrum Disorder: A Scoping Review.Journal of autism and developmental disorders · 2026Article
- AΙ-Driven Interventions for Neurocognitive, Self-Regulation, and Adaptive Skill Development in Neurodevelopmental and Cognitive Disorders: A Systematic Review of Randomized Controlled Trials.Healthcare (Basel, Switzerland) · 2026Review
- Wireless Electroencephalography in Research on Children With Developmental Disabilities: Scoping Review.Journal of medical Internet research · 2026Article
- Wearable Technologies for Clinical Assessment and Monitoring in Pediatric Neurodevelopmental Disorders : A Narrative Review.Journal of Korean Neurosurgical Society · 2026Article
- Article
- Closed-loop EEG-based neurofeedback and brain-computer interface interventions for mental health: a review.Frontiers in neuroscience · 2026Review
- Brain-computer interface: an update for the clinicians.Frontiers in human neuroscience · 2026Review
- Trends in Multimodal Artificial Intelligence for Autism Research in Children and Adolescents: A Scientometric Study.Journal of the Korean Academy of Child and Adolescent Psychiatry · 2025Review
- Integrating AI and Assistive Technologies in Healthcare: Insights from a Narrative Review of Reviews.Healthcare (Basel, Switzerland) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveBehavioral 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.
methodsA 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.
resultsAfter 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.
conclusionArtificial 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.
Indexed as
Identifiers
39565505What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.