Machine Learning to Detect Anxiety Disorders from Error-Related Negativity and EEG Signals
This is an incremental review paper that addresses the problem of improving anxiety disorder detection for mental health diagnostics by summarizing existing research.
This paper systematically reviews 54 research papers from 2013-2023 on using EEG and error-related negativity (ERN) signals to detect anxiety disorders, analyzing machine learning and deep learning methods, and concludes that robust, generic prediction methods still face challenges like task-specific setups and feature selection.
Anxiety is a common mental health condition characterised by excessive worry, fear and apprehension about everyday situations. Even with significant progress over the past few years, predicting anxiety from electroencephalographic (EEG) signals, specifically using error-related negativity (ERN), still remains challenging. Following the PRISMA protocol, this paper systematically reviews 54 research papers on using EEG and ERN markers for anxiety detection published in the last 10 years (2013 -- 2023). Our analysis highlights the wide usage of traditional machine learning, such as support vector machines and random forests, as well as deep learning models, such as convolutional neural networks and recurrent neural networks across different data types. Our analysis reveals that the development of a robust and generic anxiety prediction method still needs to address real-world challenges, such as task-specific setup, feature selection and computational modelling. We conclude this review by offering potential future direction for non-invasive, objective anxiety diagnostics, deployed across diverse populations and anxiety sub-types.