Evaluating deep learning EEG-based mental stress classification in adolescents with autism for breathing entrainment BCI

Avirath Sundaresan,Adrien Martel,Victoria Grace,Antoni Valero-Cabré,Sean Cheong,Brian Penchina

doi:10.1186/s40708-021-00133-5

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.

Highlights

Individuals with autism spectrum disorder (ASD) often demonstrate deficits in social communication skills and restricted or stereotyped behaviors and interests [1]
As the participants were prompted for their self-reported mental stress level following every stressor and breathing period, stress scores prior to the 1st stress induction period were not collected and the 1st stress induction period was not considered in the behavioral data analysis
4 Discussion To the best of our knowledge, in this study we propose for the first time a deep learning-based classifier for decoding mental stress, a complex and covert state, from scalp EEG signals in youth with ASD

Summary

Introduction

Individuals with autism spectrum disorder (ASD) often demonstrate deficits in social communication skills and restricted or stereotyped behaviors and interests [1]. This causes those with ASD to experience states of cognitive and emotional overload, leading to increased stress and anxiety symptoms [2]. Anxiety and the design of appropriate intervention methods have been identified by the autism community and clinicians as a key priority with researchers emphasizing the need for more precise measures of anxiety [14]. The lack of objective and continuous measurements of stress is detrimental for a population already affected by an inability to express inner experiences and calls for novel methods to identify individualized stress markers in real-time [15]. Among the triggers identified, such as challenging sensory experiences or social demands, anxiety related to academic expectations is thought to have the greatest impact on school performance for ASD children and adolescents [16, 17]

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