WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7150
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Article Task-Specific Dynamical Entropy Variations in EEG as a Biomarker for Parkinson's Disease Progression(Springer, 2025) Onay, Fatih; Karacali, BilgeUncovering the neuronal mechanisms un-derlying optimal behavioral performance is essential to understand how the brain dynamically adapts to changing conditions. In Parkinson's disease (PD), these neuronal mechanisms are disrupted and lead to impairments in motor coordination and higher-order cognitive functions. This study investigates neuronal dynamics during a lower-limb pedaling task by analyzing the dynamical entropy of EEG signals in healthy controls (HC), PD patients, and PD patients with freezing of gait (PDFOG). We examined both average entropy changes and entropy variability across trials to characterize task-specific neural adaptations across disease progression. Results showed that PD and PDFOG patients exhibited decreased levels of permutation entropy in frontal and parietal regions, which may be associated with loss of cognitive adapta-tion due to altered information processing. Additionally, Vasicek's entropy variability in both PD groups was significantly diminished in occipital and left frontal regions, suggesting reduced cognitive capacity to dy-namically allocate neuronal resources during task engagement. We extended this analysis to the classification of groups using LDA and SVM classifiers, where entropy-derived features achieved a classification accuracy of up to 96.15% when distinguishing HC from PDFOG patients. This dynamical entropic framework provides a novel approach for capturing neural complexity changes during task performance, revealing subtle cognitive-motor impairments in PD. Understanding the maintenance of cognitive information processing and flexibility in response to motor and cognitive task demands could be a useful tool to track PD diagnosis and progression in addition to resting-state analyses.Article Citation - WoS: 4Citation - Scopus: 3Papercraft Doppler Radar Measurements Based on Covariance Eigenvalue Spectrum-Assisted Empirical Mode Decomposition(Institute of Electrical and Electronics Engineers Inc., 2025) Atac, Enes; Onay, Fatih; Karatay, AnilDoppler radar systems encounter challenges due to their high costs, cumbersome designs, and heavy weights, especially in resource-limited environments. As a promising alternative, papercraft Doppler radar has emerged, offering a lightweight, easily deployable and cost-effective solution. However, despite many advantages, papercraft-based radar faces inherent challenges due to the material used, which leads to vulnerability to external stimuli. In this article, a novel method is proposed demonstrating that papercraft Doppler radar can achieve high performance comparable to its aluminum counterparts and perform multitarget detection even in noisy environment with multiple stimuli. For the first time, we integrate a papercraft Doppler radar with the proposed covariance eigenvalue spectrum (CES)-assisted empirical mode decomposition (EMD) method, significantly improving the performance of the papercraft radar system. Single and multitarget detection, exploiting proper intrinsic mode function (IMF) selection, is achieved through the CES algorithm, which distinguishes between the target and unwanted components via proper windowing and weighting of the decomposed radar signal. According to the results, the proposed method significantly enhances multitarget movement detection and outperforms existing methods.