ArticleBrain informatics2021
Evaluating deep learning EEG-based mental stress classification in adolescents with autism for breathing entrainment BCI.
Article in Brain informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 70 citations in OpenAlex.
- Machine learning and deep learning applied to EEG and fNIRS for early autism spectrum disorder diagnosis: a systematic review.Frontiers in psychiatry · 2026Pooled it
- Brain-Computer Interfaces: The Dawn of a New Era in Disease Treatment.Exploration (Beijing, China) · 2026Review
- Application and Research Progress of BCI in Post-Stroke Psychiatric Disorders: A Narrative Review.Medical science monitor : international medical journal of experimental and clinical research · 2026Review
- Considering whether brain-computer interfaces have prospective potential to support children who have the physical abilities for touch-based AAC access: a forum manuscript.Augmentative and alternative communication (Baltimore, Md. : 1985) · 2026Article
- A Feasibility Study of Using an In-Ear EEG System for a Quantitative Assessment of Stress and Mental Workload.Sensors (Basel, Switzerland) · 2026Article
- Leveraging AI-Driven Neuroimaging Biomarkers for Early Detection and Social Function Prediction in Autism Spectrum Disorders: A Systematic Review.Healthcare (Basel, Switzerland) · 2025Review
- Motor imagery EEG signal classification with a multivariate time series approach.Biomedical engineering online · 2023Article
- Behavioral Change Prediction from Physiological Signals Using Deep Learned Features.Sensors (Basel, Switzerland) · 2022Article
- Status of deep learning for EEG-based brain-computer interface applications.Frontiers in computational neuroscience · 2022Review
- Predicting age from resting-state scalp EEG signals with deep convolutional neural networks on TD-brain dataset.Frontiers in aging neuroscience · 2022Article
- Mental Disorder Diagnosis from EEG Signals Employing Automated Leaning Procedures Based on Radial Basis Functions.Journal of medical and biological engineering · 2022Article
- Analysis and research on the influence of music on students' mental health under the background of deep learning.Frontiers in psychology · 2022Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 3 countries.
Funding
Abstract
Mental stress is a major individual and societal burden and one of the main contributing factors that lead to pathologies such as depression, anxiety disorders, heart attacks, and strokes. Given that anxiety disorders are one of the most common comorbidities in youth with autism spectrum disorder (ASD), this population is particularly vulnerable to mental stress, severely limiting overall quality of life. To prevent this, early stress quantification with machine learning (ML) and effective anxiety mitigation with non-pharmacological interventions are essential. This study aims to investigate the feasibility of exploiting electroencephalography (EEG) signals for stress assessment by comparing several ML classifiers, namely support vector machine (SVM) and deep learning methods. We trained a total of eleven subject-dependent models-four with conventional brain-computer interface (BCI) methods and seven with deep learning approaches-on the EEG of neurotypical (n=5) and ASD (n=8) participants performing alternating blocks of mental arithmetic stress induction, guided and unguided breathing. Our results show that a multiclass two-layer LSTM RNN deep learning classifier is capable of identifying mental stress from ongoing EEG with an overall accuracy of 93.27%. Our study is the first to successfully apply an LSTM RNN classifier to identify stress states from EEG in both ASD and neurotypical adolescents, and offers promise for an EEG-based BCI for the real-time assessment and mitigation of mental stress through a closed-loop adaptation of respiration entrainment.
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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.