Stress monitoring using low-cost electroencephalogram devices: A systematic literature review

Abstract

Introduction

The use of low-cost, consumer-grade wearable health monitoring devices has become increasingly prevalent in mental health research, including stress studies. While cortisol response magnitude remains the gold standard for stress assessment, an expanding body of research employs low-cost EEG devices as primary tools for recording biomarker data, often combined with wrist and ring-based wearables. However, the technical variability among low-cost EEG devices, particularly in sensor count and placement according to the 10-20 Electrode Placement System, poses challenges for reproducibility in study outcomes.

Objective

This review aims to provide an overview of the growing application of low-cost EEG devices and machine learning techniques for assessing brain function, with a focus on stress detection. It also highlights the strengths and weaknesses of various machine learning methods commonly used in stress research, and evaluates the reproducibility of reported findings along with sensor count and placement importance.

Methods

A comprehensive review was conducted of published studies utilizing EEG devices for stress detection and their associated machine learning approaches. Searches were performed across databases including Scopus, Google Scholar, ScienceDirect, Nature, and PubMed, yielding 69 relevant articles for analysis. The selected studies were synthesized into four thematic categories: stress assessment using EEG, low-cost EEG devices, datasets for EEG-based stress measurement, and machine learning techniques for EEG-based stress analysis. For machine learning-focused studies, validation and reproducibility methods were critically assessed. Study quality was evaluated and scored using the IJMEDI checklist.

Results

The review identified several studies employing low-cost EEG devices to monitor brain activity during stress and relaxation phases, with many reporting high predictive accuracy using various machine learning validation techniques. However, only 54% of the studies included health screening prior to experimentation, and 58% were categorized as low-powered due to limited sample sizes. Additionally, few studies validated their results using an independent validation set or cortisol response as a correlating biomarker and there was a lack of consensus on data pre-processing and sensor placement as a key contributor to improving model generalization and accuracy.

Conclusion

Low-cost consumer-grade wearable devices, including EEG and wrist-based monitors, are increasingly utilized in stress-related research, offering promising avenues for non-invasive biomarker monitoring. However, significant gaps remain in standardizing EEG signal processing and sensor placement, both of which are critical for enhancing model generalization and accuracy. Furthermore, the limited use of independent validation sets and cortisol response as correlating biomarkers highlights the need for more robust validation methodologies. Future research should focus on addressing these limitations and establishing consensus on data pre-processing techniques and sensor configurations to improve the reliability and reproducibility of findings in this growing field.