用脑电图和错误相关负波预测焦虑，系统梳理了十年研究进展。
Machine Learning to Detect Anxiety Disorders from Error-Related Negativity and EEG Signals
- 基于EEG与ERN信号，整合传统机器学习与深度学习方法。
- 54篇论文分析显示，模型在任务特异性上仍存在局限。
- 适合心理诊断、脑机接口与精神健康技术开发者参考。
焦虑是一种常见心理健康问题，表现为对日常情境的过度担忧、恐惧和不安。尽管近年来取得显著进展，但仅通过脑电图（EEG）信号，特别是错误相关负波（ERN）来预测焦虑仍具挑战性。本文遵循PRISMA协议，系统回顾了2013至2023年间发表的54篇关于使用EEG与ERN标记物进行焦虑检测的研究。分析表明，支持向量机、随机森林等传统机器学习，以及卷积神经网络、循环神经网络等深度学习模型在不同数据类型中被广泛应用。然而，构建稳健且通用的焦虑预测方法仍需应对真实场景中的任务特异性、特征选择与计算建模等挑战。本文最后提出未来非侵入式、客观焦虑诊断的发展方向，适用于多样化人群与焦虑亚型。
原文摘要 · Abstract (English)
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.
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