Research Excellence Award
Ozlem Karabiber Cura
Izmir Katip Celebi University, Turkey
Ozlem Karabiber Cura is a Turkish academic researcher and assistant professor affiliated with Izmir Katip Celebi University. Her scholarly work focuses on biomedical signal processing, electroencephalography (EEG) analysis, deep learning, machine learning applications in healthcare, and computational biomedical engineering. Her research portfolio demonstrates consistent contributions to neurological disorder detection, artificial intelligence-assisted diagnosis systems, and time-frequency signal processing methodologies.[1] Her academic publications include peer-reviewed journal articles, conference proceedings, and book chapters addressing applications of deep learning and biomedical data analysis for epilepsy, Alzheimer’s disease, ADHD, dyslexia, and related neuropsychiatric disorders.[2]
Abstract
This article documents the academic profile and scientific contributions of Ozlem Karabiber Cura in the fields of biomedical engineering, machine learning, and biomedical signal analysis. Her research activities primarily involve EEG-based neurological disorder classification using deep learning architectures, signal decomposition techniques, brain connectivity analysis, and computer-aided diagnostic systems. Through interdisciplinary biomedical engineering research, she has contributed to developments in epilepsy detection, Alzheimer’s disease recognition, ADHD classification, and neuropsychiatric signal interpretation using artificial intelligence methodologies.[3]
Keywords
Biomedical Signal Processing; Electroencephalography; Deep Learning; Machine Learning; Artificial Intelligence; EEG Classification; Alzheimer’s Disease; ADHD Detection; Biomedical Engineering; Time-Frequency Analysis; Neural Systems; Brain Connectivity; Biomedical Data Analysis.
Introduction
The integration of artificial intelligence and biomedical engineering has become increasingly significant in healthcare analytics and clinical decision support systems. Within this interdisciplinary environment, Ozlem Karabiber Cura has developed research contributions centered on advanced biomedical signal analysis, deep learning-driven diagnostic systems, and EEG-based computational modeling.[4] Her academic work reflects ongoing developments in signal decomposition methodologies, neurological disorder classification systems, and data-driven biomedical interpretation frameworks that support modern clinical research applications.
Her educational and professional trajectory includes engineering studies in electrical-electronics engineering and biomedical engineering, followed by doctoral research in biomedical technologies at Izmir Katip Celebi University.[1] She currently serves as Assistant Professor within the Department of Biomedical Engineering and contributes to undergraduate and graduate education in biomedical signal processing, adaptive signal analysis, and machine learning applications.[5]
Research Profile
Ozlem Karabiber Cura’s research profile demonstrates sustained engagement in biomedical data analytics, machine learning, and EEG signal processing. Her scientific output includes peer-reviewed journal publications, conference proceedings, collaborative projects, and scholarly book chapters focused on neurological signal interpretation and computational diagnostics.[6]
Her research projects have addressed topics including attention deficit hyperactivity disorder (ADHD), epilepsy, Alzheimer’s dementia, dyslexia, wearable motion capture systems, and medical imaging applications. Several projects involved deep learning-based image interpretation, time-frequency signal representations, and graph-theoretical approaches for functional brain network analysis.[7]
In addition to research activities, she has supervised graduate-level studies related to deep learning analysis of sEMG data and neuropsychiatric disorder detection using EEG signals and artificial intelligence methodologies.[1]
Research Contributions
A major component of Karabiber Cura’s research contributions involves EEG signal decomposition and classification using machine learning and deep learning methods. Her studies have investigated synchrosqueezing transforms, intrinsic time-scale decomposition, empirical mode decomposition, dynamic mode decomposition, and time-frequency representations for neurological disorder analysis.[8]
Her published studies on ADHD detection using EEG feature maps and deep learning methods contributed to the application of artificial intelligence techniques for clinical neuroscience research.[9] Additional work involving Alzheimer’s dementia classification and epileptic seizure analysis further expanded the use of deep learning architectures for biomedical signal interpretation.[10]
Karabiber Cura has also participated in interdisciplinary biomedical engineering projects involving wearable technologies, medical image analysis, X-ray fracture detection systems, and functional brain network analysis. Her collaborative publications demonstrate methodological diversity and engagement with emerging computational approaches in healthcare engineering.[11]
Publications
The scholarly publications of Ozlem Karabiber Cura include contributions to journals such as International Journal of Neural Systems, Biocybernetics and Biomedical Engineering, Digital Signal Processing, Computers and Electrical Engineering, and Machine Learning: Science and Technology.[12]
Selected publications include studies on EEG-based ADHD classification, epilepsy signal interpretation, deep learning approaches for Alzheimer’s disease detection, and biomedical signal decomposition frameworks. Her conference contributions include presentations at EUSIPCO, CoDIT, TIPTEKNO, and related international biomedical engineering conferences.[13]
- Detection of Attention Deficit Hyperactivity Disorder based on EEG feature maps and deep learning.
- Classification of Epileptic EEG Signals Using Synchrosqueezing Transform and Machine Learning.
- Detection of Alzheimer’s Dementia by Using Signal Decomposition and Machine Learning Methods.
- Electroencephalography-based classification of individuals with ADHD using brain connectivity information and deep learning.
- Time-frequency signal processing: Today and future.
Research Impact
The research impact of Ozlem Karabiber Cura is reflected through her indexed publications, citation record, interdisciplinary collaborations, and conference dissemination activities. According to Scopus-based metrics, her profile includes 52 indexed documents, 474 citations by 439 documents, and an h-index of 9.[14]
Her research has contributed to the broader development of AI-assisted biomedical diagnostics and neurological signal analysis. The integration of machine learning techniques into clinical EEG interpretation systems remains a relevant area within computational medicine and healthcare engineering research communities.
The diversity of her publication venues and research collaborations demonstrates sustained academic engagement in signal processing, biomedical engineering, and artificial intelligence applications. Her educational activities and graduate supervision additionally support academic capacity development in biomedical data science.[5]
Award Suitability
The academic profile of Ozlem Karabiber Cura demonstrates alignment with the objectives of the International Research Data Analysis Excellence & Awards event. Her contributions to biomedical machine learning, EEG-based classification systems, and healthcare-oriented artificial intelligence applications reflect active participation in contemporary data-driven scientific research.
Her publication record, project participation, conference activity, and interdisciplinary collaborations indicate continued involvement in biomedical innovation and computational healthcare research. The integration of deep learning techniques with biomedical signal processing methods further supports her relevance to research excellence recognition frameworks in data analysis and AI applications.
Conclusion
Ozlem Karabiber Cura has established a scholarly profile centered on biomedical engineering, machine learning, and computational neuroscience applications. Her work contributes to the advancement of EEG signal analysis, artificial intelligence-supported diagnosis systems, and biomedical data interpretation methodologies. Through publications, collaborative research projects, graduate supervision, and conference participation, she continues to contribute to interdisciplinary biomedical engineering research and healthcare-oriented machine learning applications.
External Links
References
- Izmir Katip Celebi University. (2025). Academic curriculum vitae and researcher profile of Ozlem Karabiber Cura.
https://ikcu.edu.tr/
- Elsevier. (n.d.). Scopus author details: Ozlem Karabiber Cura, Author ID 57195223021. Scopus.
https://www.scopus.com/authid/detail.uri?authorId=57195223021
- Karabiber Cura, O., Akan, A., & Türe, H. S. (2023). Classification of Epileptic and Psychogenic Nonepileptic Seizures via Time–Frequency Features of EEG Data.
https://doi.org/10.1142/S0129065723500454
- Akan, A., & Karabiber Cura, O. (2021). Time–frequency signal processing: Today and future. Digital Signal Processing.
https://doi.org/10.1016/j.dsp.2021.103216
- Karabiber Cura, O., Kocaaslan Atli, S., & Akan, A. (2023). Attention deficit hyperactivity disorder recognition based on intrinsic time-scale decomposition of EEG signals.
https://doi.org/10.1016/j.bspc.2022.104512
- Karabiber Cura, O., Dervisoglu, S., & Eroglu, G. (2026). Graph Theory Metrics-based Functional Brain Network Analysis in Dyslexia.
https://doi.org/10.7212/karaelmasfen.1796249
- Karabiber Cura, O., & Akan, A. (2021). Classification of Epileptic EEG Signals Using Synchrosqueezing Transform and Machine Learning.
https://doi.org/10.1142/S0129065721500052
- Karabiber Cura, O., Akan, A., & Kocaaslan Atli, S. (2024). Detection of Attention Deficit Hyperactivity Disorder based on EEG feature maps and deep learning.
https://doi.org/10.1016/j.bbe.2024.07.003
- Karabiber Cura, O., Akan, A., Cosku Yilmaz, G., & Türe, H. S. (2022). Detection of Alzheimer’s Dementia by Using Signal Decomposition and Machine Learning Methods.
https://doi.org/10.1142/S0129065722500423
- Catal, M. S., Gumus, A., Karabiber Cura, O., et al. (2025). Vision-language model approach for few-shot learning of attention deficit hyperactivity disorder using EEG connectivity-based featured images.
https://doi.org/10.1088/2632-2153/ae15e5
- Ozdemir, M. A., Karabiber Cura, O., & Akan, A. (2021). Epileptic EEG Classification by Using Time-Frequency Images for Deep Learning.
https://doi.org/10.1142/S012906572150026X
- International Research Data Analysis Excellence & Awards. (2026). Research excellence and data analysis recognition platform.
https://researchdataanalysis.com/
- Karabiber Cura, O., Ciklacandir, F. G., & Eroglu, G. (2024). Diagnosis of Dyslexia using 2D EEG Feature Map and Convolutional Neural Network.
https://doi.org/10.1109/TIPTEKNO63488.2024.10755456
- Karabiber Cura, O., & Akan, A. (2021). Analysis of epileptic EEG signals by using dynamic mode decomposition and spectrum.
https://doi.org/10.1016/j.bbe.2020.11.002