Evidence map›Paper›PMID 42845955›Full record

ArticleFrontiers in systems neuroscience2026

Music-evoked EEG signals for mental health assessment using two- and three-class classification.

Abdelkader Nasreddine Belkacem, Abdelhadi Hireche, Nour Faris Ali, Mohamed Atef, Abdalla A R M Hamid, Ibrahim M Elfadel, Herbert F Jelinek

Abstract read
In one paragraph

Article in Frontiers in systems neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Abdelkader Nasreddine BelkacemDepartment of Computer and Network Engineering, United Arab Emirates University, Al Ain/Abu Dhabi, United Arab Emirates.
Abdelhadi HirecheDepartment of Computer and Network Engineering, United Arab Emirates University, Al Ain/Abu Dhabi, United Arab Emirates.
Nour Faris AliBiomedical Engineering Program, College of Engineering, American University of Sharjah, Sharjah, United Arab Emirates.
Mohamed AtefElectrical and Communication Engineering Department, College of Engineering, United Arab Emirates University, Al Ain/Abu Dhabi, United Arab Emirates.
Abdalla A R M HamidDepartment of Clinical Psychology, United Arab Emirates University, Al Ain/Abu Dhabi, United Arab Emirates.
Ibrahim M ElfadelDepartment of Computer and Information Engineering, Khalifa University, Abu Dhabi, United Arab Emirates.
Herbert F JelinekDepartment of Medical Sciences, Khalifa University, Abu Dhabi, United Arab Emirates.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Electroencephalography (EEG), a non-invasive method of measuring brain activity, has become a valuable tool in mental health research for examining neural activity associated with various psychological disorders and neurofeedback therapies, including music therapy. However, the use of EEG under music and no-music conditions for mental health screening remains insufficiently validated. Methods: In this preliminary study, we implemented an experimental protocol in which 48 participants underwent EEG recordings during both music and no-music sessions with identical relaxation instructions. The Patient Health Questionnaire-9 (PHQ-9) and the Generalized Anxiety Disorder 7-item (GAD-7) scale were also included. Five complementary EEG features were extracted for each mental fatigue class: power spectral density (PSD) across different frequency bands, band powers, alpha-theta ratio, theta-beta ratio, and channel coherence. Binary classification (mental fatigue) and multi-output binary classification (anxiety, depression, and stress components) were implemented using a deep residual multi-layer perceptron (MLP), a feedforward neural network with residual skip connections. Leave-Subject-Out cross-validation ensured robust generalization, with all metrics computed at the subject level rather than epoch level. Results: For binary classification, the model achieved 72.92% accuracy in the music condition and 58.33% accuracy in the no-music condition, with notably high recall (96.67%) in the music condition. Multi-output classification in the music condition showed stress with high accuracy (93.75%) but low AUC (35.56%), reflecting the high prevalence of the stressed class (85.7%), while anxiety and depression presented greater challenges (54.17% and 62.50% accuracy, respectively). Discussion: These preliminary results suggest that music may modulate brain activity patterns in ways that enhance the discriminability of mental fatigue states, although the fixed condition order means order and habituation effects cannot be ruled out.

Indexed as

anxietydeep learningdeep residual MLPdepressionelectroencephalography (EEG)mental fatiguemusic therapy assessmentstress

Identifiers

PMID42845955
PMCPMC13642751

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.