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

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.