A Systematic Review of EEG Studies on the Neural Effects of Quran Listening
Bibliographic record
Abstract
Objective: This systematic review aims to explore the effects of listening to the Quran on the electrophysiological aspects of the human brain, particularly focusing on how this auditory experience influences cognitive function, emotional well-being, and mental health. Method: We conducted a comprehensive search across multiple databases, including Web of Science, PubMed, Scopus, and Google Scholar, using keywords such as "Quran" and "EEG." Studies included in this review were observational or clinical trials that investigated the effects of Quran listening on brain activity using EEG. Eligibility criteria were assessed according to predefined standards, with a focus on studies published in English. The Newcastle-Ottawa Scale was employed to evaluate the quality of the selected articles, and data extraction followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Results: A total of 236 studies were evaluated, leading to the inclusion of 22 eligible studies in this review. Findings indicate that listening to Quranic verses is associated with increased alpha and theta power, which correlates with relaxation and improved emotional states in participants, including non-Muslims. The review identified significant variations in study designs, methodologies, and quality, with many studies displaying a high risk of bias. Conclusion: Listening to Quranic verses demonstrates potential therapeutic effects by activating brain regions associated with relaxation and emotional regulation. Despite promising findings, the current body of research is limited, particularly regarding nonlinear EEG dynamics and comprehensive study designs. Further neuroimaging and clinical investigations are warranted to validate these results and explore the therapeutic applications of Quranic listening in diverse contexts, such as pain management, psychological health, and rehabilitation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".