Neurostressology: A systematic review of EEG-based automated mental stress perspectives
Sayantan Acharya, Abbas Khosravi, Douglas Creighton, Roohallah Alizadehsani, U Rajendra Acharya
Abstract
Presently, mental stress is a significant contributor to physical and psychological health issues, making its early detection and monitoring a public health priority. Among various neuroimaging methods, Electroencephalography (EEG) has emerged as a promising tool due to its ability to capture fine-grained temporal dynamics associated with cognitive stress responses. This paper presents a systematic review of 275 peer-reviewed studies published between 2003 and January 2025, focused on EEG-based mental stress quantification. While previous stress reviews primarily emphasized signal processing pipelines and psychological stress methods, this study emphasizes the potential of fusion-centric approaches. It systematically analyzes and compares studies across multiple dimensions, including EEG datasets, stressor categories, key electrodes, brain regions, feature correlations, and classifier performance, to identify methodological trends, inconsistencies, and gaps in standardization. The review underlines multiple levels of fusion, including multimodal fusion such as EEG with speech-based features, algorithmic fusion, and fusion of transfer learning and feature extraction using multimodal foundation models. It also examines key challenges in reproducibility, dataset availability, differences in brain region selection, experiment duration, and EEG processing approaches. Among classifiers, SVM , random forest, and decision tree are identified as the most effective AI methods for stress classification, with CNN and LSTM showing superior performance in capturing spatiotemporal patterns. This review concludes by highlighting the importance of fusing cortical activation patterns with EEG-based connectivity measures and deep learning techniques to enhance the accuracy of mental stress detection.