MACHINE LEARNING TO DETECT ANXIETY DISORDERS FROM ERROR-RELATED NEGATIVITY AND EEG SIGNALS - Systematic Review Using EEG and ERN Markers

Aug 5, 2026

Anxiety is a prevalent mental health condition marked by excessive worry, fear, and apprehension, yet reliable prediction from electroencephalographic (EEG) signals remains difficult, especially when relying on error-related negativity (ERN). Following the PRISMA protocol, the study systematically reviews 54 papers from 2013–2023 on EEG and ERN markers for anxiety detection. The review summarizes usage of traditional machine learning and deep learning, and identifies needs for robust, generic approaches addressing real-world constraints like task setup, feature selection, and computational modelling for non-invasive diagnostics across populations and anxiety sub-types.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages15

Reading Time38 min

AccessFree to download

Introduction

Anxiety disorders and clinical limitations

Machine learning for mental health and EEG-based detection

Systematic review methodology

PRISMA-based search and selection process

Data organization by EEG models and ERN statistical analysis

Selected research contributions

Traditional ML and deep learning approaches

Real-world challenges and future directions

arXiv :2410 .00028v1 [ ee ss . SP] 16 Sep 2024 MACHINE LEARNING TO DETECT ANXIETY DISORDERS FROM ERROR-RELATED NEGATIVITY AND EEG SIGNALS Ramya Chandrasekar 1 , Md Rakibul Hasan 1,2 , Shreya Ghosh 1 , Tom Gedeon 1,3 , and Md Zakir Hossain 1 1 School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Bentley, WA 6102, Australia 2Department of Electrical and Electronic Engineering, BRAC University, Dhaka 1212, Bangladesh 3 Óbuda University, Budapest, Hungary [Ramya.Chandrasekar@student.curtin.edu.au](Ramya.Chandrasekar@student.curtin.edu.au) , {Rakibul.Hasan, Shreya.Ghosh, Tom .Gedeon, [Zakir.Hossain1}@curtin.edu.au](Zakir.Hossain1}@curtin.edu.au) ABSTRACT 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 realworld 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 populationsand anxiety sub-types. Keywords machine learning · deep learning · EEG, error-related negativity · anxiety · detection 1 Introduction Anxiety is endemic to every person, with an occurrence rate of approximately 20%[World Health Organization, 2017] . Between 2020 and 2022, over one in six people (17.2% or 3.4 million people) aged 16 to 85 years experienced an anxiety disorder [Australian Bureau of Statistics] . Anxiety is caused by changes in the situation, nervousness and common symptoms, including sweating, trembling and excessive worrying, which affect a person’s daily life. Anxiety disorders encompass a range of conditions, such as generalised anxiety disorder (GAD), panic disorder (PD), social anxiety disorder (SAD), obsessive-compulsive disorder (OCD), various phobia-related disorders, physical pain related protective behaviour [Li et al., 2020, 2021] and depression [Ghosh and Anwar, 2021] . Current clinical approaches for diagnosing these disorders often suffer from limitations in accuracy and objectivity, relying heavily on self-reports, patient histories and clinical observations. These methods can be subjective and may not capture the nuanced neural and behavioural patterns associated with anxiety, leading to potential misdiagnoses. Recent research has shown promising results in using machine learning techniques to detect anxiety through physiological analysis [Abd-Alrazaq et al., 2023], such as respiration, electrocardiogram (ECG), photoplethysmography (PPG), electrodermal response (EDA) and electroencephalography (EEG), to identify patterns associated with anxiety states [Abd-Alrazaq et al., 2023] . GAD SAD PD OCD Anxiety Disorder Types Figure 1: Annual prevalence rates of four major types of anxiety disorder [National Institute of Mental Health] . Abbreviations: GAD (Generalised anxiety disorder), SAD (Social anxiety disorder), OCD (Obsessive-compulsive disorder), PD (Panic disorder) . Machine learning techniques are increasingly employed in mental health to understand complex patterns [Meyer et al., 2015] . Machine learning models can be analysed in various data types, including physiological si

FAQs

Why is detecting anxiety from EEG signals, particularly using ERN, challenging?

Prediction remains challenging because anxiety-related neural patterns are difficult to capture reliably from EEG and ERN signals under practical conditions. The review emphasizes unresolved real-world issues affecting robustness and generalisation.

How many studies does the review include and what period does it cover?

The review follows PRISMA and selects 54 papers published from 2013 to 2023.

What machine learning methods are most commonly used in the reviewed anxiety-detection studies?

The review highlights frequent use of traditional models such as support vector machines and random forests, alongside deep learning models including convolutional neural networks and recurrent neural networks.

What future directions are proposed for non-invasive objective anxiety diagnostics?

The review calls for developing robust and generic methods that address task-specific setup, feature selection, and computational modelling, enabling deployment across diverse populations and anxiety sub-types.

Aug 5, 2026

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