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International Conference on Brain Informatics

BI 2020: Brain Informatics pp 227–238Cite as

Deep LSTM Recurrent Neural Network for Anxiety Classification from EEG in Adolescents with Autism

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12241))

Abstract

Anxiety is common in youth with autism spectrum disorder (ASD), causing unique lifelong challenges that severely limit everyday opportunities and reduce quality of life. Given the detrimental consequences and long-term effects of pervasive anxiety for childhood development and the covert nature of mental states, brain-computer interfaces (BCIs) represent a promising method to identify maladaptive states and allow for individualized and real-time mitigatory action to alleviate anxiety. Here we investigated the effects of slow paced breathing entrainment during stress induction on the perceived levels of anxiety in neurotypical adolescents and adolescents with autism, and propose a multi-class long short-term recurrent neural net (LSTM RNN) deep learning classifier capable of identifying anxious states from ongoing electroencephalography (EEG) signals. The deep learning classifier used was able to discriminate between anxious and non-anxious classes with an accuracy of 90.82% and yielded an average accuracy of 93.27% across all classes. Our study is the first to successfully apply an LSTM RNN classifier to identify anxious states from EEG. This LSTM RNN classifier holds promise for the development of neuroadaptive systems and individualized intervention methods capable of detecting and alleviating anxious states in both neurotypical adolescents and adolescents with autism.

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Author information

Authors and Affiliations

  1. The Nueva School, San Mateo, CA, 94033, USA

    Brian Penchina, Avirath Sundaresan & Sean Cheong

  2. Causal Brain Dynamics, Plasticity and Rehabilitation Team, Frontlab, Brain and Spine Institute, ICM, Paris, France

    Adrien Martel

Authors
  1. Brian Penchina
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  2. Avirath Sundaresan
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  3. Sean Cheong
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  4. Adrien Martel
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Corresponding author

Correspondence to Adrien Martel .

Editor information

Editors and Affiliations

  1. Nottingham Trent University, Nottingham, UK

    Mufti Mahmud

  2. University of Padua, Padua, Italy

    Stefano Vassanelli

  3. Jahangirnagar University, Dhaka, Bangladesh

    M. Shamim Kaiser

  4. Maebashi Institute of Technology, Maebashi, Japan

    Ning Zhong

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Penchina, B., Sundaresan, A., Cheong, S., Martel, A. (2020). Deep LSTM Recurrent Neural Network for Anxiety Classification from EEG in Adolescents with Autism. In: Mahmud, M., Vassanelli, S., Kaiser, M.S., Zhong, N. (eds) Brain Informatics. BI 2020. Lecture Notes in Computer Science(), vol 12241. Springer, Cham. https://doi.org/10.1007/978-3-030-59277-6_21

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  • DOI: https://doi.org/10.1007/978-3-030-59277-6_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59276-9

  • Online ISBN: 978-3-030-59277-6

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