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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 8625

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Prof. Dr. Jae-Ho Han

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1. Department of Brain and Cognitive Engineering, Korea University, 145 Anam Rd., Seoul 02841, Republic of Korea
2. Department of Artificial Intelligence, Korea University, 145 Anam Rd., Seoul 02841, Republic of Korea
Interests: artificial intelligence in biomedicine; diagnosis of retinal diseases; deep learning for ophthalmology images; neuroscience research
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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

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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 (8 papers)

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22 pages, 2225 KiB

Open AccessArticle

Connectogram-COH: A Coherence-Based Time-Graph Representation for EEG-Based Alzheimer’s Disease Detection

by Ehssan Aljanabi and İlker Türker

Diagnostics 2025, 15(11), 1441; https://doi.org/10.3390/diagnostics15111441 - 5 Jun 2025

Viewed by 212

Abstract

Background: Alzheimer’s disease (AD) is a neurological disorder that affects the brain in the elderly, resulting in memory loss, mental deterioration, and loss of the ability to think and act, while being a cause of death, with its rates increasing dramatically. A popular method to detect AD is electroencephalography (EEG) signal analysis thanks to its ability to reflect neural activity, which helps to identify abnormalities associated with the disorder. Originating from its multivariate nature, EEG signals are generally handled as multidimensional time series, and the related methodology is employed. Methods: This study proposes a new transformation strategy that generates a graph representation with time resolution, which handles EEG recordings as relatively small time windows and converts these segments into a similarity graph based on signal coherence between available channels. The retrieved adjacency matrices are further flattened to form a 1-pixel image column, which represents the coherence activity from the available electrodes within the given time window. These pixel columns are concatenated horizontally for all available sliding time windows with 50% overlap, resulting in a grayscale image representation that can be input to well-known deep learning architectures specialized for images. We name this representation Connectogram-COH, a coherence-based version of the previously proposed time graph representation, Connectogram. Results: The experimental results demonstrate that the proposed Connectogram-COH representation effectively captures the coherence dynamics of multichannel EEG data and achieves high accuracy in detecting Alzheimer’s disease. The time graph images serve as robust input for deep learning classifiers, outperforming traditional EEG representations in terms of classification performance. Conclusions: Connectogram-COH offers a powerful and interpretable approach for transforming EEG signals into image representations that are well suited for deep learning. The method not only improves the detection of AD but also shows promise for broader applications in EEG-based and general time series classification tasks. Full article

(This article belongs to the Special Issue EEG Analysis in Diagnostics)

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15 pages, 1097 KiB

Open AccessArticle

Contribution of the EEG in the Diagnostic Workup of Patients with Transient Neurological Deficit and Acute Confusional State at the Emergency Department: The EMINENCE Study

by Maenia Scarpino, Antonello Grippo, Maria Teresa Verna, Francesco Lolli, Benedetta Piccardi, Peiman Nazerian, Patrizia Nencini, Carmela Ielapi and Andrea Nencioni

Diagnostics 2025, 15(7), 863; https://doi.org/10.3390/diagnostics15070863 - 28 Mar 2025

Viewed by 417

Abstract

Background/Objectives: To investigate the usefulness of an emergency electroencephalogram (emEEG) in the differential diagnosis of transient neurological deficits (TND) and acute confusional state (ACS). Methods: An analysis was performed on a subset of patients included in EMINENCE, a retrospective study of subjects admitted to the Emergency Department (ED) of our tertiary hospital over a 1-year period. The analysis was limited to patients with neurological symptoms/signs compatible with cerebral hemispheric origin or with an ACS of <24 h duration. We evaluated the usefulness of the emEEG in the diagnostic workup of TND and ACS. Results: Speech disorder (75.3%), hyposthenia (68.1%), and ACS (62.9%) were the signs/symptoms with the highest percentage of abnormal emEEGs, especially concerning epileptic discharges. Seizures (85.7%) and encephalopathy (74.3%) were the final diagnoses with the highest percentage of abnormal emEEGs, particularly epileptic discharges and focal slow waves in patients discharged with a diagnosis of seizures, and bilateral slow waves and generalized periodic discharges with triphasic morphology (GPDTM) in patients discharged with a diagnosis of encephalopathy. The presence/absence of epileptic discharges associated with focal slow waves could discriminate between seizures and vascular disease, especially in hyposthenia (100% of seizures when epileptic discharges were present, vs. 50% when absent). Migraine with aura (66%) and an unknown diagnosis (56%) were the final diagnoses with the most normal emEEG. The rapid timing of the emEEG recording compared to the patient’s admission allowed us to perform the test in 29.5% of patients who were still symptomatic, of whom 79% had an abnormal emEEG. Conclusions: The emEEG mainly contributed to the diagnosis when speech disorder, hyposthenia, and ACS were the admission signs/symptoms, especially for the final diagnosis of seizures and encephalopathy. Full article

(This article belongs to the Special Issue EEG Analysis in Diagnostics)

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14 pages, 2680 KiB

Open AccessArticle

Electroencephalography-Based Neuroinflammation Diagnosis and Its Role in Learning Disabilities

by Günet Eroğlu

Diagnostics 2025, 15(6), 764; https://doi.org/10.3390/diagnostics15060764 - 18 Mar 2025

Cited by 1 | Viewed by 703

Abstract

Background/Objectives: Learning disabilities (LDs) are complex neurodevelopmental conditions influenced by genetic, epigenetic, and environmental factors. Recent research suggests that maternal autoimmune conditions, perinatal stress, and vitamin D deficiency may contribute to neuroinflammation, which, in turn, can disrupt brain development. Chronic neuroinflammation, driven by activated microglia and astrocytes, has been associated with synaptic dysfunction and cognitive impairment, potentially impacting learning and memory processes. This study aims to explore the relationship between neuroinflammation and LDs, emphasizing the role of electroencephalography (EEG) biomarkers in early diagnosis and intervention. Methods: A systematic analysis was conducted to examine the prevalence, core symptoms, and typical age of diagnosis of LDs. EEG biomarkers, particularly theta, gamma, and alpha power, were assessed as indicators of neuroinflammatory states. Additionally, artificial neural networks (ANNs) were employed to classify EEG patterns associated with LDs, evaluating their diagnostic accuracy. Results: Findings indicate that EEG biomarkers can serve as potential indicators of neuroinflammatory patterns in children with LDs. ANNs demonstrated high classification accuracy in distinguishing EEG signatures related to LDs, highlighting their potential as a diagnostic tool. Conclusions: EEG-based biomarkers, combined with machine learning approaches, offer a non-invasive and precise method for detecting neuroinflammatory patterns associated with LDs. This integrative approach advances precision medicine by enabling early diagnosis and targeted interventions for neurodevelopmental disorders. Further research is required to validate these findings and establish standardized diagnostic protocols. Full article

(This article belongs to the Special Issue EEG Analysis in Diagnostics)

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26 pages, 2375 KiB

Open AccessArticle

CubicPat: Investigations on the Mental Performance and Stress Detection Using EEG Signals

by Ugur Ince, Yunus Talu, Aleyna Duz, Suat Tas, Dahiru Tanko, Irem Tasci, Sengul Dogan, Abdul Hafeez Baig, Emrah Aydemir and Turker Tuncer

Diagnostics 2025, 15(3), 363; https://doi.org/10.3390/diagnostics15030363 - 4 Feb 2025

Cited by 2 | Viewed by 965

Abstract

Background\Objectives: Solving the secrets of the brain is a significant challenge for researchers. This work aims to contribute to this area by presenting a new explainable feature engineering (XFE) architecture designed to obtain explainable results related to stress and mental performance using electroencephalography (EEG) signals. Materials and Methods: Two EEG datasets were collected to detect mental performance and stress. To achieve classification and explainable results, a new XFE model was developed, incorporating a novel feature extraction function called Cubic Pattern (CubicPat), which generates a three-dimensional feature vector by coding channels. Classification results were obtained using the cumulative weighted iterative neighborhood component analysis (CWINCA) feature selector and the t-algorithm-based k-nearest neighbors (tkNN) classifier. Additionally, explainable results were generated using the CWINCA selector and Directed Lobish (DLob). Results: The CubicPat-based model demonstrated both classification and interpretability. Using 10-fold cross-validation (CV) and leave-one-subject-out (LOSO) CV, the introduced CubicPat-driven model achieved over 95% and 75% classification accuracies, respectively, for both datasets. Conclusions: The interpretable results were obtained by deploying DLob and statistical analysis. Full article

(This article belongs to the Special Issue EEG Analysis in Diagnostics)

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16 pages, 8853 KiB

Open AccessArticle

The Practical Implications of Re-Referencing in ERP Studies: The Case of N400 in the Picture–Word Verification Task

by Vojislav Jovanović, Igor Petrušić, Vanja Ković and Andrej M. Savić

Diagnostics 2025, 15(2), 156; https://doi.org/10.3390/diagnostics15020156 - 11 Jan 2025

Viewed by 1111

Abstract

Background: The selection of an optimal referencing method in event-related potential (ERP) research has been a long-standing debate, as it can significantly influence results and lead to data misinterpretation. Such misinterpretation can produce flawed scientific conclusions, like the inaccurate localization of neural processes, and in practical applications, such as using ERPs as biomarkers in medicine, it may result in incorrect diagnoses or ineffective treatments. In line with the development and advancement of good scientific practice (GSP) in ERP research, this study sought to address several questions regarding the most suitable digital reference for investigating the N400 ERP component. Methods: The study was conducted on 17 neurotypical participants. Based on previous research, the references evaluated included the common average reference (AVE), mean earlobe reference (EARS), left mastoid reference (L), mean mastoids reference (MM), neutral infinity reference (REST), and vertex reference (VERT). Results: The results showed that all digital references, except for VERT, successfully elicited the centroparietal N400 effect in the picture–word verification task. The AVE referencing method showed the most optimal set of metrics in terms of effect size and localization, although it also produced the smallest difference waves. The most similar topographic dynamics in the N400 window were observed between the AVE and REST referencing methods. Conclusions: As the most optimal regions of interest (ROI) for the picture–word elicited N400 effect, nine electrode sites spanning from superior frontocentral to parietal regions were identified, showing consistent effects across all referencing methods except VERT. Full article

(This article belongs to the Special Issue EEG Analysis in Diagnostics)

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21 pages, 2070 KiB

Open AccessArticle

Zipper Pattern: An Investigation into Psychotic Criminal Detection Using EEG Signals

by Gulay Tasci, Prabal Datta Barua, Dahiru Tanko, Tugce Keles, Suat Tas, Ilknur Sercek, Suheda Kaya, Kubra Yildirim, Yunus Talu, Burak Tasci, Filiz Ozsoy, Nida Gonen, Irem Tasci, Sengul Dogan and Turker Tuncer

Diagnostics 2025, 15(2), 154; https://doi.org/10.3390/diagnostics15020154 - 11 Jan 2025

Cited by 4 | Viewed by 1001

Abstract

Background: Electroencephalography (EEG) signal-based machine learning models are among the most cost-effective methods for information retrieval. In this context, we aimed to investigate the cortical activities of psychotic criminal subjects by deploying an explainable feature engineering (XFE) model using an EEG psychotic criminal dataset. Methods: In this study, a new EEG psychotic criminal dataset was curated, containing EEG signals from psychotic criminal and control groups. To extract meaningful findings from this dataset, we presented a new channel-based feature extraction function named Zipper Pattern (ZPat). The proposed ZPat extracts features by analyzing the relationships between channels. In the feature selection phase of the proposed XFE model, an iterative neighborhood component analysis (INCA) feature selector was used to choose the most distinctive features. In the classification phase, we employed an ensemble and iterative distance-based classifier to achieve high classification performance. Therefore, a t-algorithm-based k-nearest neighbors (tkNN) classifier was used to obtain classification results. The Directed Lobish (DLob) symbolic language was used to derive interpretable results from the identities of the selected feature vectors in the final phase of the proposed ZPat-based XFE model. Results: To obtain the classification results from the ZPat-based XFE model, leave-one-record-out (LORO) and 10-fold cross-validation (CV) methods were used. The proposed ZPat-based model achieved over 95% classification accuracy on the curated EEG psychotic criminal dataset. Moreover, a cortical connectome diagram related to psychotic criminal detection was created using a DLob-based explainable artificial intelligence (XAI) method. Conclusions: In this regard, the proposed ZPat-based XFE model achieved both high classification performance and interpretability. Thus, the model contributes to feature engineering, psychiatry, neuroscience, and forensic sciences. Moreover, the presented ZPat-based XFE model is one of the pioneering XAI models for investigating psychotic criminal/criminal individuals. Full article

(This article belongs to the Special Issue EEG Analysis in Diagnostics)

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29 pages, 4818 KiB

Open AccessArticle

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 - 26 Nov 2024

Cited by 3 | Viewed by 1037

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 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

Open AccessArticle

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

Cited by 2 | Viewed by 2207

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 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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