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Diagnostics
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EEG Analysis in Diagnostics, 2nd Edition
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EEG Analysis in Diagnostics, 2nd Edition

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 1707

Editor

Prof. Dr. Jae-Ho Han

E-Mail Website
Guest Editor
1. Department of Brain and Cognitive Engineering, Korea University, Seoul 136-701, Republic of Korea
2. Department of Artificial Intelligence, Korea University, Seoul 136-701, Republic of Korea
Interests: artificial intelligence in biomedicine; diagnosis of retinal diseases; deep learning for ophthalmology images; neuroscience research
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Electroencephalography (EEG) is a noninvasive and essential tool in neuroscience, providing profound insights into the intricate workings of the brain and offering a comprehensive view of its dynamic functions. For instance, EEG plays a pivotal role in diagnosing and monitoring various conditions such as epilepsy, sleep disorders, brain tumors, and cognitive impairments. Its analysis provides clinicians with valuable insights into brain functionality, aiding them in making informed treatment decisions. Furthermore, EEG contributes to the collective knowledge of neurological mechanisms, catalyzing progress in medical science.

This Special Issue, entitled 'EEG Analysis in Diagnostics, 2nd Edition', aims to highlight the diverse and multifaceted applications of EEG. The focus of this Special Issue is on the use of EEG technology in clinical settings for accurate diagnoses and the effective management of neurological disorders, as well as its role in research environments. We welcome contributions that align with these themes or delve into related research endeavors, such as the recent advancements in EEG technology, novel analytical techniques, and their implications for understanding complex pathophysiology.

Prof. Dr. Jae-Ho Han
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • advanced EEG Techniques
  • the characterization of EEG signal patterns
  • the identification of EEG signal abnormalities
  • functional connectivity analysis in EEG
  • sleep studies based on EEG
  • EEG in epilepsy diagnosis and monitoring
  • neurofeedback and EEG in cognitive enhancement
  • event-related potentials in clinical EEG
  • understanding pathophysiology via EEG

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Published Papers (2 papers)

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13 pages, 2119 KB  
Article
Morphological Remodeling of Scalp High-Frequency Oscillations Across BASED-Stratified Groups in Infantile Epileptic Spasms Syndrome
by Keisuke Maeda, Shunta Yamaguchi, Himari Tsuboi, Naohiro Ichino, Keisuke Osakabe, Keiko Sugimoto, Gen Furukawa and Naoko Ishihara
Diagnostics 2026, 16(13), 2024; https://doi.org/10.3390/diagnostics16132024 - 29 Jun 2026
Viewed by 126
Abstract
Background/Objectives: High-frequency oscillations (HFOs)—transient electroencephalography (EEG) activity above 80 Hz—are emerging biomarkers of infantile epileptic spasms syndrome (IESS). However, the relationship between their multidimensional characteristics and clinical severity remains poorly understood. This study aimed to clarify the association of scalp HFO morphology [...] Read more.
Background/Objectives: High-frequency oscillations (HFOs)—transient electroencephalography (EEG) activity above 80 Hz—are emerging biomarkers of infantile epileptic spasms syndrome (IESS). However, the relationship between their multidimensional characteristics and clinical severity remains poorly understood. This study aimed to clarify the association of scalp HFO morphology with severity across levels defined by the Burden of Amplitudes and Epileptiform Discharges (BASED) score, an interictal EEG grading scale for IESS. Methods: We enrolled 53 children with epilepsy (30 with IESS and 23 non-IESS controls) and quantified HFO frequency, duration, amplitude, and cycle count from automatically detected scalp HFOs during interictal EEG. Results: Patient-level median analyses demonstrated significant monotonic associations with BASED severity: HFO frequency decreased (Spearman ρ = −0.46, p = 0.001) and duration increased (ρ = 0.32, p = 0.026). Event-level mixed-effects models confirmed these findings, showing that frequency decreased by 10.6 Hz per BASED step (p < 0.001) and duration increased 1.18-fold per step (p = 0.011), whereas amplitude and cycle count showed no consistent associations. Phenotype-level enrichment analysis revealed that specific morphological signatures significantly distinguished severity levels, with severe IESS showing a marked reduction in the high-frequency/high-amplitude/short-duration class (OR = 0.49, 95% CI 0.33–0.73) and a shift toward low-frequency/long-duration phenotypes. Conclusions: Scalp HFOs showed lower frequencies and longer durations in higher BASED-stratified groups, suggesting that HFO morphology may provide quantitative information complementary to visual EEG assessment in IESS. These findings support the potential utility of HFO phenotypic stratification for objective evaluation and longitudinal monitoring of disease burden. Full article
(This article belongs to the Special Issue EEG Analysis in Diagnostics, 2nd Edition)
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30 pages, 3878 KB  
Article
MS-MDDNet: A Lightweight Deep Learning Framework for Interpretable EEG-Based Diagnosis of Major Depressive Disorder
by Rabeah AlAqel, Muhammad Hussain and Saad Al-Ahmadi
Diagnostics 2026, 16(2), 363; https://doi.org/10.3390/diagnostics16020363 - 22 Jan 2026
Viewed by 1137
Abstract
Background: Major Depressive Disorder (MDD) is a pervasive psychiatric condition. Electroencephalography (EEG) is employed to detect MDD-specific neural patterns because it is non-invasive and temporally precise. However, manual interpretation of EEG signals is labor-intensive and subjective. This problem was addressed by proposing [...] Read more.
Background: Major Depressive Disorder (MDD) is a pervasive psychiatric condition. Electroencephalography (EEG) is employed to detect MDD-specific neural patterns because it is non-invasive and temporally precise. However, manual interpretation of EEG signals is labor-intensive and subjective. This problem was addressed by proposing machine learning (ML) and deep learning (DL) methods. Although DL methods are promising for MDD detection, they face limitations, including high model complexity, overfitting due to subject-specific noise, excessive channel requirements, and limited interpretability. Methods: To address these challenges, we propose MS-MDDNet, a new lightweight CNN model specifically designed for EEG-based MDD detection, along with an ensemble-like method built on it. The architecture of MS-MDDNet incorporates spatial, temporal, and depth-wise separable convolutions, along with average pooling, to enhance discriminative feature extraction while maintaining computational efficiency with a small number of learnable parameters. Results: The method was evaluated using 10-fold Cross-Subjects Cross-Validation (CS-CV), which mitigates the risks of overfitting associated with subject-specific noise, thereby contributing to generalization robustness. Across three public datasets, the proposed method achieved performance comparable to state-of-the-art approaches while maintaining lower computational complexity. It achieved a 9% improvement on the MODMA dataset, with an accuracy of 99.33%, whereas on MUMTAZ and PRED + CT it achieved accuracies of 98.59% and 96.61%, respectively. Conclusions: The predictions of the proposed method are interpretable, with interpretability achieved through correlation analysis between gamma energy and learned features. This makes it a valuable tool for assisting clinicians and individuals in diagnosing MDD with confidence, thereby enhancing transparency in decision-making and promoting clinical credibility. Full article
(This article belongs to the Special Issue EEG Analysis in Diagnostics, 2nd Edition)
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