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.

Anxiety is one of the most common comorbidities in youth with autism spectrum disorder (ASD), severely limiting academic opportunities and overall quality of life. In the present study, we compared several machine learning classifiers, namely support vector machine (SVM) and deep learning methods, in order to evaluate the feasibility of an EEG-based BCI for the real-time assessment and mitigation of anxiety through a closed-loop adaptation of respiration entrainment. We trained a total of eleven subject-dependent models—four with conventional 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. We propose a multiclass two-layer LSTM RNN deep learning classifier capable of identifying anxious states from ongoing EEG with an overall accuracy of 93.27%. Our study is the first to successfully apply an LSTM RNN classifier to identify anxious states from EEG in both neurotypical adolescents and adolescents with ASD.

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