Evaluating deep learning EEG-based mental stress classification in adolescents with autism for breathing entrainment BCI
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 di...
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doaj-18a357e45f5249ca960232b86dcf57182021-07-18T11:09:57ZengSpringerOpenBrain Informatics2198-40182198-40262021-07-018111210.1186/s40708-021-00133-5Evaluating deep learning EEG-based mental stress classification in adolescents with autism for breathing entrainment BCIAvirath Sundaresan0Brian Penchina1Sean Cheong2Victoria Grace3Antoni Valero-Cabré4Adrien Martel5The Nueva SchoolThe Nueva SchoolThe Nueva SchoolMuvik Labs, LLCCenter for Computer Research in Music and Acoustics, Stanford UniversityCognitive Neuroscience and Information Technology Research Program, Open University of Catalonia (UOC)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.https://doi.org/10.1186/s40708-021-00133-5Mental stressAutismEEGDeep learningBreathing entrainment |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Avirath Sundaresan Brian Penchina Sean Cheong Victoria Grace Antoni Valero-Cabré Adrien Martel |
spellingShingle |
Avirath Sundaresan Brian Penchina Sean Cheong Victoria Grace Antoni Valero-Cabré Adrien Martel Evaluating deep learning EEG-based mental stress classification in adolescents with autism for breathing entrainment BCI Brain Informatics Mental stress Autism EEG Deep learning Breathing entrainment |
author_facet |
Avirath Sundaresan Brian Penchina Sean Cheong Victoria Grace Antoni Valero-Cabré Adrien Martel |
author_sort |
Avirath Sundaresan |
title |
Evaluating deep learning EEG-based mental stress classification in adolescents with autism for breathing entrainment BCI |
title_short |
Evaluating deep learning EEG-based mental stress classification in adolescents with autism for breathing entrainment BCI |
title_full |
Evaluating deep learning EEG-based mental stress classification in adolescents with autism for breathing entrainment BCI |
title_fullStr |
Evaluating deep learning EEG-based mental stress classification in adolescents with autism for breathing entrainment BCI |
title_full_unstemmed |
Evaluating deep learning EEG-based mental stress classification in adolescents with autism for breathing entrainment BCI |
title_sort |
evaluating deep learning eeg-based mental stress classification in adolescents with autism for breathing entrainment bci |
publisher |
SpringerOpen |
series |
Brain Informatics |
issn |
2198-4018 2198-4026 |
publishDate |
2021-07-01 |
description |
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. |
topic |
Mental stress Autism EEG Deep learning Breathing entrainment |
url |
https://doi.org/10.1186/s40708-021-00133-5 |
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