Advances in Electroencephalography for Post-Traumatic Stress Disorder Identification: A Scoping Review

Summary & key facts

Researchers reviewed 73 studies from 2013 to 2024 that used EEG — a way to record brain waves — to help find, tell apart, or treat PTSD. They mapped how studies collected EEG, what brain signals they measured, and which computer methods they used. The review found that brainwave frequency bands and brief brain responses to events were the main tools. Some specific signals, like the Alpha band and an event-related response called LPP, worked well for spotting PTSD, and another response called P300 helped tell PTSD apart from other conditions. Computer models that learn from labeled examples showed very high accuracy in some studies, but most research used military veterans and only three studies shared their data openly. The authors conclude EEG methods look promising as more objective tools, but we need bigger, more diverse datasets and better methods before these tools can be trusted for wide use.

Key facts:

• The review looked at 73 studies published between 2013 and 2024 from major databases like Scopus, Web of Science, and PubMed.
• Of those 73 studies, 52 focused on diagnosis, 8 focused on telling PTSD apart from other conditions, and 15 looked at therapy or treatment effects.
• Most studies used EEG techniques that measure brainwave frequency bands and event-related brain responses, which are brief brain reactions to specific events or stimuli.
• The Alpha brainwave band showed strong performance in both diagnosis studies and therapy studies.
• An event-related potential called LPP was the most effective signal reported for diagnosing PTSD, while the P300 response was most useful for differentiating PTSD from other conditions.
• Supervised machine-learning models called support vector machines — which learn from labeled examples — reached very high reported accuracy in some studies, roughly 99.7% for diagnosis, about 84% for differentiation, and about 78% for psychotherapy-related tasks.
• Random Forest models that combined EEG with other signals like heart activity, skin conductance, or speech also performed very well, showing about 99% accuracy in at least one multimodal setup.
• Some researchers used unsupervised methods — algorithms that look for natural groups in the data — to try to find PTSD subtypes or to separate PTSD from other mental disorders without pre-labeling examples.
• Most study participants were veterans or combatants, which means the results may not apply well to the general public or to people from different backgrounds.
• Only three studies reported open datasets, and the review flagged gaps such as limited use of sleep measures, limited full-band EEG analysis, and uneven use of event-related potentials across studies.

Abstract

Background: Post-traumatic stress disorder (PTSD) is a psychophysiological condition caused by traumatic experiences. Its diagnosis typically relies on subjective tools like clinical interviews and self-reports. Objectives: This scoping review analyzes computational methods using EEG signal processing for PTSD diagnosis, differentiation, and therapy. It provides a comprehensive overview of the entire EEG analysis pipeline, from acquisition to statistical and machine learning techniques for PTSD diagnosis. Methods: Using the PRISMA-ScR protocol, studies published between 2013 and 2024 were reviewed from databases including Scopus, Web of Science, and PubMed. A total of 73 studies were analyzed: 52 on diagnosis, 8 on differentiation, and 15 on therapy. Results: EEG Bands and Event-Related Potentials (ERP) were the dominant techniques. The Alpha band demonstrated strong performance in diagnosis and therapy. LPP ERP was most effective for diagnosis, and P300 for differentiation. Supervised SVM models achieved the highest accuracy in diagnosis (ACC = 0.997), differentiation (ACC = 0.841), and psychotherapy (ACC = 0.78). Random Forest multimodal models integrating EEG with other modalities (e.g., ECG, GSR, Speech) achieved ACC = 0.993. Unsupervised approach is employed to cluster patients to identify PTSD subtypes or to differentiate PTSD from other mental disorders. Veterans and combatants were the primary study population, and only three studies reported open datasets. Conclusions: EEG-based methods hold promise as objective tools for PTSD diagnosis and therapy. The review identified limitations in the use of ERP, sleep characterization and full-band EEG. Broader datasets representing diverse populations are essential to mitigate bias and facilitate robust inter-model comparisons. Future research should focus on deep learning, adaptive signal decomposition, and multimodal approaches.

Topics

EEG and Brain-Computer Interfaces Functional Brain Connectivity Studies Heart Rate Variability and Autonomic Control

Categories

Cognitive Neuroscience Life Sciences Neuroscience

Tags

Biology Botany Clinical psychology Electroencephalography Identification (biology) Linguistics Philosophy Psychiatry Psychology Stress (linguistics) Traumatic stress

Conditions & symptoms

PTSD Poor sleep