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Diagnostics
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EEG Analysis in Diagnostics
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EEG Analysis in Diagnostics

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

Deadline for manuscript submissions: 31 August 2025 | Viewed by 1842

Special Issue 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', 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 100 words) can be sent to the Editorial Office for announcement on this website.

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-blind 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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29 pages, 4818 KiB  
Article
ChMinMaxPat: Investigations on Violence and Stress Detection Using EEG Signals
by Omer Bektas, Serkan Kirik, Irem Tasci, Rena Hajiyeva, Emrah Aydemir, Sengul Dogan and Turker Tuncer
Diagnostics 2024, 14(23), 2666; https://doi.org/10.3390/diagnostics14232666 (registering DOI) - 26 Nov 2024
Abstract
Background and Objectives: Electroencephalography (EEG) signals, often termed the letters of the brain, are one of the most cost-effective methods for gathering valuable information about brain activity. This study presents a new explainable feature engineering (XFE) model designed to classify EEG data for [...] Read more.
Background and Objectives: Electroencephalography (EEG) signals, often termed the letters of the brain, are one of the most cost-effective methods for gathering valuable information about brain activity. This study presents a new explainable feature engineering (XFE) model designed to classify EEG data for violence detection. The primary objective is to assess the classification capability of the proposed XFE model, which uses a next-generation feature extractor, and to obtain interpretable findings for EEG-based violence and stress detection. Materials and Methods: In this research, two distinct EEG signal datasets were used to obtain classification and explainable results. The recommended XFE model utilizes a channel-based minimum and maximum pattern (ChMinMaxPat) feature extraction function, which generates 15 distinct feature vectors from EEG data. Cumulative weight-based neighborhood component analysis (CWNCA) is employed to select the most informative features from these vectors. Classification is performed by applying an iterative and ensemble t-algorithm-based k-nearest neighbors (tkNN) classifier to each feature vector. Information fusion is achieved through iterative majority voting (IMV), which consolidates the 15 tkNN classification results. Finally, the Directed Lobish (DLob) symbolic language generates interpretable outputs by leveraging the identities of the selected features. Together, the tkNN classifier, IMV-based information fusion, and DLob-based explainable feature extraction transform the model into a self-organizing explainable feature engineering (SOXFE) framework. Results: The ChMinMaxPat-based model achieved over 70% accuracy on both datasets with leave-one-record-out (LORO) cross-validation (CV) and over 90% accuracy with 10-fold CV. For each dataset, 15 DLob strings were generated, providing explainable outputs based on these symbolic representations. Conclusions: The ChMinMaxPat-based SOXFE model demonstrates high classification accuracy and interpretability in detecting violence and stress from EEG signals. This model contributes to both feature engineering and neuroscience by enabling explainable EEG classification, underscoring the potential importance of EEG analysis in clinical and forensic applications. Full article
(This article belongs to the Special Issue EEG Analysis in Diagnostics)
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18 pages, 3818 KiB  
Article
Integrating EEG and Ensemble Learning for Accurate Grading and Quantification of Generalized Anxiety Disorder: A Novel Diagnostic Approach
by Xiaodong Luo, Bin Zhou, Jiaqi Fang, Yassine Cherif-Riahi, Gang Li and Xueqian Shen
Diagnostics 2024, 14(11), 1122; https://doi.org/10.3390/diagnostics14111122 - 28 May 2024
Viewed by 1359
Abstract
Current assessments for generalized anxiety disorder (GAD) are often subjective and do not rely on a standardized measure to evaluate the GAD across its severity levels. The lack of objective and multi-level quantitative diagnostic criteria poses as a significant challenge for individualized treatment [...] Read more.
Current assessments for generalized anxiety disorder (GAD) are often subjective and do not rely on a standardized measure to evaluate the GAD across its severity levels. The lack of objective and multi-level quantitative diagnostic criteria poses as a significant challenge for individualized treatment strategies. To address this need, this study aims to establish a GAD grading and quantification diagnostic model by integrating an electroencephalogram (EEG) and ensemble learning. In this context, a total of 39 normal subjects and 80 GAD patients were recruited and divided into four groups: normal control, mild GAD, moderate GAD, and severe GAD. Ten minutes resting state EEG data were collected for every subject. Functional connectivity features were extracted from each EEG segment with different time windows. Then, ensemble learning was employed for GAD classification studies and brain mechanism analysis. Hence, the results showed that the Catboost model with a 10 s time window achieved an impressive 98.1% accuracy for four-level classification. Particularly, it was found that those functional connections situated between the frontal and temporal lobes were significantly more abundant than in other regions, with the beta rhythm being the most prominent. The analysis framework and findings of this study provide substantial evidence for the applications of artificial intelligence in the clinical diagnosis of GAD. Full article
(This article belongs to the Special Issue EEG Analysis in Diagnostics)
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