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.Article Citation - WoS: 6Citation - Scopus: 10Using Chemosensory-Induced Eeg Signals To Identify Patients With <i>de Novo</I> Parkinson's Disease(Elsevier Sci Ltd, 2024) Olcay, Orkan; Onay, Fatih; Ozturk, Guliz Akin; Oniz, Adile; Ozgoren, Murat; Hummel, Thomas; Guducu, CagdasObjective: Parkinson's disease (PD) patients generally exhibit an olfactory loss. Hence, psychophysical or electrophysiological tests are used for diagnosis. However, these tests are susceptible to the subjects' behavioral response bias and require advanced techniques for an accurate analysis. Proposed Approach: Using well-known feature extraction methods, we characterized chemosensory-induced EEG responses of the participants to classify whether they have PD. The classification was performed for different time intervals after chemosensory stimulation to see which temporal segment better separates healthy controls and subjects with de novo PD. Results: The performances show that entropy and connectivity features discriminate effectively PD and HC participants when olfactory-induced EEG signals were used. For these methods, discrimination is over 80% for segments 100-700 and 200-800 milliseconds after stimulus onset. Comparison with Existing Methods: We compared the performance of our framework with linear predictive coding, bispectrum, wavelet entropy-based methods, and TDI score-based classification. While the entropy- and connectivity-based methods elicited the highest classification performances for olfactory stimuli, the linear predictive coding-based method elicited slightly higher performance than our framework when the trigeminal stimuli were used. Conclusion: This is one of the first studies that use chemosensory-induced EEG signals along with different feature extraction methods to classify healthy subjects and subjects with de novo PD. Our results show that entropy and functional connectivity methods unravel the chemosensory-induced neural dynamics encapsulating critical information about the subjects' olfactory performance. Furthermore, time- and frequency-resolved feature analysis is beneficial for capturing disease-affected neural patterns.Conference Object Citation - Scopus: 2Parkinson hastalığı sınıflandırmasına yönelik ivmeölçer tabanlı zamanlama analizi(IEEE, 2023) Karaçalı, Bilge; Onay, FatihParkinson's disease is a neurodegenerative disorder caused by dopamine deficiency in the basal ganglia, resulting in cognitive and motor impairments. In this study, accelerometer signals were used to estimate the delay time between the command to start pedaling and the actual movement onset in three groups: healthy individuals (n=13), Parkinson's disease patients (n=13), and patients with freezing of gait symptoms (n=13). Features were extracted from the delay time distributions for each participant and subjected to a triple classification. Linear support vector machine achieved a classification accuracy of 69.2% for all participants. Notably, the average time to start pedaling was found to be significantly different among the three groups, and accelerometer-based timing analysis could be used as a diagnostic tool to assist clinical tests.Conference Object Ampute Elektromiyografi Sinyallerinin Evrişimli Sinir Ağları Kullanılarak Sınıflandırılması(IEEE, 2020) Onay, Fatih; Mert, AhmetThe classification of EMG signals for the amputees is important to develop a powered-prosthetic that is capable of replacing with lost limbs. The EMG signals collected from residual limbs reduce the classification accuracy due to muscle movements that cannot be realized properly. In this study, classification performance is aimed to be increased by combining CNN with root mean square (RMS) and waveform length (WL) that are used in analysis of EMG signals successfully. The features such as RMS and WL extracted from EMG signals for the classification of six hand movements at the low, medium, and high force levels were applied to CNN input, and classification results were compared with nearest neighbour and linear discriminant analysis.Article Citation - WoS: 22Citation - Scopus: 26Phasor Represented Emg Feature Extraction Against Varying Contraction Level of Prosthetic Control(Elsevier, 2020) Onay, Fatih; Mert, AhmetThis paper introduces phasor representation of electromyography (EMG) feature extraction (PRE). The well-known EMG signal analysis methods, namely root mean square (RMS), and waveform length (WL) are adopted into phasor form depending electrode placement. The values of these methods are computed from 8-channel EMG signals, and their magnitudes with respect to origin are used to construct phasor represented features in this study. The class separability of the PRE is strengthened by adding difference EMG and Euclidean distanced phasor in order to obtain improved feature set against force and electrode variations. The simulations (three schemes) are performed on publicly available EMG dataset on transradial amputees, and the results are presented in terms of accuracy and processing time considering the control strategies of a prosthetic hand. Linear (LDA), and quadratic (QDA) discriminant analysis, and knearest neighbor (k-NN) classifiers are trained, and tested by the PRE features. Our method outperforms previous accuracy rates in some cases, and reaches to accuracy results of the first study using this dataset without using any reduction method. In our simulations, accuracy rates up to 71.17% (PRE with QDA) for six classes hand movements with three force levels are obtained decreasing processing time by 81.83%. (C) 2020 Elsevier Ltd. All rights reserved.