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Olcay, Bilal Orkan
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Orkan Olcay, B.
Olcay, B. Orkan
Olcay, B. O.
Olcay, Bilal Orkan
Olcay, B. Orkan
Olcay, B. O.
Olcay, Bilal Orkan
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bilalolcay@iyte.edu.tr
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01.01. Units Affiliated to the Rectorate
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1NO POVERTY
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2ZERO HUNGER
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3GOOD HEALTH AND WELL-BEING
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4QUALITY EDUCATION
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6CLEAN WATER AND SANITATION
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8DECENT WORK AND ECONOMIC GROWTH
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9INDUSTRY, INNOVATION AND INFRASTRUCTURE
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5
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12
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54
Publication Collaboration
| Affiliation Name | Count |
|---|---|
| Izmir Institute of Technology | 10 |
| Dokuz Eylül University | 5 |
| Ege University | 4 |
| Near East University | 3 |
| Technische Universität Dresden | 3 |
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54
Scopus Citation Count
72
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WoS Citations per Publication
4.15
Scopus Citations per Publication
5.54
Open Access Source
7
Supervised Theses
2
| Journal | Count |
|---|---|
| Biomedical Signal Processing and Control | 3 |
| 25th Signal Processing and Communications Applications Conference (SIU) -- MAY 15-18, 2017 -- Antalya, TURKEY | 2 |
| 24th Signal Processing and Communication Application Conference, SIU 2016 | 1 |
| Brain Research | 1 |
| Computers in Biology and Medicine | 1 |
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Doctoral Thesis On the Characterization of Motor Imagery Functions Based on Systematic Timing Organization of the Human Brain(01. Izmir Institute of Technology, 2021) Olcay, Bilal Orkan; Karaçalı, Bilge; Karaçalı, Bilge; 01.01. Units Affiliated to the Rectorate; 03.05. Department of Electrical and Electronics Engineering; 01. Izmir Institute of Technology; 03. Faculty of EngineeringThe main objective of this thesis is to analyze the timing organization of the brain. The human brain is known to adjust its localized and also the reciprocal operations for each different cognitive task adaptively. This flexibility of the brain has attracted considerable interest in neuroscience. Elucidation of timing adaptation property of brain, however, remains as unresolved due to dynamically changing and nonlinear nature of the brain. In this thesis, we characterize the timing organization of the brain during motor imagery activity using electroencephalography signals. First, we propose a novel motor imagery activity recognition method that relies on the activity-specific time-lag between electroencephalography signals obtained from different brain regions. Next, we generalize this approach into three-parameter formulation to determine the timing profiles of activity-specific short-lived synchronization. The identification of activity-specific timing parameters was carried out using a heuristic approach that maximizes the average pairwise channel synchronizations during associated activity periods. Thereafter, we propose a novel BCI framework that find and use the timings of electroencephalography signals of localized brain regions that elicit localized activity-specific features. We identify the timings for each different brain regions by adopting a heuristic-probabilistic method. Finally, we propose a novel autoregressive modeling framework that finds a representative model for each different cognitive activity. We demonstrated the efficacy of the proposed methods on publicly available brain-computer interfacing datasets on motor imagery. The performance results indicate that considering the timing organization of the brain is crucial for accurate characterization of cognitive activity. In addition, it may also account for the inconsistency of brain computer interfacing performance obtained from different subjects.Conference Object Citation - Scopus: 3Entropik Kümeleme Kullanılarak Beyin Aktivitesi Karakterizasyonu(IEEE, 2017) Olcay, Bilal Orkan; Karaçalı, Bilge; Ozgoren, Murat; Guducu, Cagda; 01.01. Units Affiliated to the Rectorate; 03.05. Department of Electrical and Electronics Engineering; 01. Izmir Institute of Technology; 03. Faculty of EngineeringIn this study, two novel entropy and mutual information based algorithms have been proposed to characterize the stimulus specific brain activity. In the first method, inter channel mutual information of electroencephalography signals has been calculated and the channels that exhibit synchronized behaivour during stimulus. In the second method, the responsiveness of the individual channels has been characterized in an entropic manner and then, the channels which demonstrates stimulus specific entropic behavior have been obtained. The performance of the proposed methods has been simulated on a real dataset obtained from Dokuz Eylul University Brain Biophysics laboratory. The results demonstrate that different regions of the brain exhibit a coherent activity during stimulus.Article Citation - WoS: 14Citation - Scopus: 17Evaluation of Synchronization Measures for Capturing the Lagged Synchronization Between Eeg Channels: a Cognitive Task Recognition Approach(Elsevier, 2019) Olcay, Bilal Orkan; Olcay, Bilal Orkan; Karaçalı, Bilge; Karaçalı, Bilge; 01.01. Units Affiliated to the Rectorate; 03.05. Department of Electrical and Electronics Engineering; 01. Izmir Institute of Technology; 03. Faculty of EngineeringDuring cognitive, perceptual and sensory tasks, connectivity profile changes across different regions of the brain. Variations of such connectivity patterns between different cognitive tasks can be evaluated using pairwise synchronization measures applied to electrophysiological signals, such as electroencephalography (EEG). However, connectivity-based task recognition approaches achieving viable recognition performance have been lacking from the literature. By using several synchronization measures, we identify time lags between channel pairs during different cognitive tasks. We employed mutual information, cross correntropy, cross correlation, phase locking value, cosine similarity and nonlinear interdependence measures. In the training phase, for each type of cognitive task, we identify the time lags that maximize the average synchronization between channel pairs. These lags are used to calculate pairwise synchronization values with which we construct the train and test feature vectors for recognition of the cognitive task carried out using Fisher's linear discriminant (FLD) analysis. We tested our framework in a motor imagery activity recognition scenario on PhysioNet Motor Movement/Imagery and BCI Competition-III IVa datasets. For PhysioNet dataset, average performance results ranging between % 51 and % 61 across 20 subjects. For BCI Competition-III dataset, we achieve an average recognition performance of % 76 which is above the minimum reliable communication rate (% 70). We achieved an average accuracy over the minimum reliable communication rate on the BCI Competition-III dataset. Performance levels were lower on the PhysioNet dataset. These results indicate that a viable task recognition system is achievable using pairwise synchronization measures evaluated at the proper task specific lags.Master Thesis Analysis of Olfactory Evoked Potentials(Izmir Institute of Technology, 2014) Olcay, Bilal Orkan; Olcay, Bilal Orkan; Savacı, Ferit Acar; Savaci, Ferit Acar; 01.01. Units Affiliated to the Rectorate; 03.05. Department of Electrical and Electronics Engineering; 01. Izmir Institute of Technology; 03. Faculty of EngineeringWith the growing opportunities of laboratories and measurement techniques, cognitive science attracts many researchers interest from other branches of science. In the literature, lack of studies related to the brain's responsiveness against the olfactory stimuli has been the main source of motivation for our work on this issue. In this thesis, it is examined by means of time-dependent wavelet entropy of Electroencephalographic (EEG) signals which is collected from individuals that how olfactory and trigeminal effective odor stimuli affects responsiveness of the brain. Significance and meaningfulness of the results are shown with statistical tests of average entropy in the discrete time windows. Due to its nature of small amplitude in comparison with ongoing EEG activity, it’s hard to observe the components of olfactory evoked potentials and trigeminal evoked potentials. In order to separate these components from ongoing EEG, different signal processing techniques have been employed in this thesis. And, findings from these techniques have been conveyed to statistical tests to determine the most suitable technique for that purpose. Additionally, a novel smell performance identification metric have been offered for clinical studies that is not affected by basal activity of brain and subjective review, for objective assessment of smell performance. Statistical test result have shown that, results of this technique which is performed on 19 participants, and their TDI scores obtained from Sniffin’ Stick test battery, are in a strong correlation.Conference Object Dalgacık gürültü giderme yöntemiyle mikrodalga bileşen karakterizasyonunun iyileştirilmesi(IEEE, 2023) Karatay, Anıl; Yaman, Fatih; Olcay, Bilal Orkan; 01. Izmir Institute of Technology; 01.01. Units Affiliated to the Rectorate; 03.05. Department of Electrical and Electronics Engineering; 03. Faculty of EngineeringIn this study, an efficient approach is presented to improve the characterization of various microwave components commonly used in communication and radar applications, such as antennas and power dividers. The components were initially simulated and then fabricated using the Computer Simulation Technology (CST) software. Vector Network Analyzer (VNA) measurements of the fabricated components were performed using a low-cost but noisy coaxial cable, and the measurement results were processed using a wavelet-based noise reduction method. For comparison purposes, the Haar and Daubechies-4 (DB4) wavelet functions were applied separately, and the results were examined. It was observed that the correlation and root mean square error between the ideal and measurement results improved in a positive direction with the noise reduction application. This approach provides significant cost and labor advantages, particularly when expensive elements such as gold and silver are used in coaxial cables that are physically free from noise. The experimental and numerical results show good agreement between the ideal simulation results and the filtered measurement results.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; Onay, Fatih; Olcay, Bilal Orkan; Ozgoren, Murat; Hummel, Thomas; Guducu, Cagdas; 03.05. Department of Electrical and Electronics Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology; 01.01. Units Affiliated to the RectorateObjective: 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 Algıda gecikme ve kısa-ömürlü senkronizasyon temelli yeni bir hayali motor aktivite tanıma yaklaşımı(IEEE, 2023) Olcay, B. Orkan; Karaçalı, Bilge; Karaçalı, Bilge; Olcay, Bilal Orkan; 01.01. Units Affiliated to the Rectorate; 03.05. Department of Electrical and Electronics Engineering; 01. Izmir Institute of Technology; 03. Faculty of EngineeringThis study proposes a novel approach for investigating a brain-computer interface that considers the temporal organization of brain activity, explicitly accounting for perception latency. To this end, we aligned the onset of task periods with the concurrence of left parietal and parieto-occipital electrodes to obtain the timings of perception latencies. Then, activity-specific synchronization timings between channel pairs were calculated using the time-aligned task periods. The perception latency and activity-specific synchronization timings were subsequently used for feature extraction and classification. The proposed approach achieved significantly better performance when comparing the proposed approach with the method that did not account for the perception latencyConference Object Citation - WoS: 1Citation - Scopus: 1Eeg Verisinde Kanallar-arası Zaman Uyumluluk Profilleri Kullanılarak Hayali Hareket Tanıma(Institute of Electrical and Electronics Engineers Inc., 2016) Olcay, Bilal Orkan; Özgören, Murat; Olcay, Bilal Orkan; Karaçalı, Bilge; 01.01. Units Affiliated to the Rectorate; 03.05. Department of Electrical and Electronics Engineering; 01. Izmir Institute of Technology; 03. Faculty of EngineeringBu çalışmada, elektrotlar arası zaman gecikmesi kullanılarak bir beyin-bilgisayar ara yüzü çalışması gerçekleştirilmiştir. Öznitelik olarak, seçilen referans kanalı ile geriye kalan tüm kanalların çapraz kovaryansının mutlak değerinin en yüksek olduğu zaman gecikmeleri hesaplanmıştır. Çalışmada kullanılan 5 kişi içinden 3 kişinin sınıflandırma performansının %100’e yakın olmasının yanında bu kişilerin eğitim veri seti sayısının diğer iki kişiye göre oldukça düşük olması ve literatürde buna benzer çalışmaların azlığı, önerilen yaklaşımın geliştirilmeye açık olduğunu göstermektedir.Article Citation - WoS: 2Citation - Scopus: 2Temporal Electroencephalography Features Unveiled Via Olfactory Stimulus as Biomarkers for Mild Alzheimer's Disease(Elsevier Sci Ltd, 2025) Karaçalı, Bilge; Olcay, Bilal Orkan; Karacali, Bilge; 01.01. Units Affiliated to the Rectorate; 03.05. Department of Electrical and Electronics Engineering; 01. Izmir Institute of Technology; 03. Faculty of EngineeringAim: Our primary aim is to capture and use the timings of the characteristic brain responses to olfactory stimulation for mild Alzheimer's disease diagnosis purposes. Proposed method: Our method identifies the timings of short-lived signal segments where characteristic distances between pre- and post-stimulus relative spectral energies are attained for each EEG channel and frequency band. These timings and timing-derived features were subsequently used in a leave-one-subject-out cross-validation scenario to assess the diagnostic performance of our framework. We evaluated seven distinct statistical distance measures to determine the most effective one for characterizing the neurological conditions of the subjects. Results: The average cross-validation performance shows that our framework achieved 87.50% diagnosis performance. The frequently used features were mainly derived from the delta and alpha activity of the prefrontal region (Fp1) and the beta activity of the parietal region (Pz), which agree with the current findings of olfaction biophysics. Comparison with existing methods: We compared the performance of our method with that of four existing methods in the literature. Our method outperformed these four methods. Moreover, our method elicited the highest accuracy when the clinical olfactory score (UPSIT) was included as a feature. Conclusions: Our analysis framework reveals a significant alteration of the timing organization of the brain that emerged upon olfactory stimulation in Alzheimer's patients. The timings of characteristic response and the features calculated via these timings contribute to Alzheimer's disease diagnosis performance remarkably. The perspective proposed here may facilitate early diagnosis, thereby facilitating the exploration of novel therapeutic and treatment strategies.Conference Object Citation - WoS: 1Citation - Scopus: 1Deneysel Mod Ayrıştırması Uygulanmış Yazma Hareket Bilgisi Kullanılarak El Yazısı Karakter Tanıma(IEEE, 2017) Olcay, Bilal Orkan; Ünlü, Mehmet Zübeyir; Unlu, Mehmet Zubeyir; 03.05. Department of Electrical and Electronics Engineering; 01.01. Units Affiliated to the Rectorate; 01. Izmir Institute of Technology; 03. Faculty of EngineeringIn this paper, handwritten character recognition by using characters' writing movements is investigated. To obtain the information about writing movements a 3-axis accelerometer is used. Just like most of other sensors, 3-axis accelerometers give the actual movement signal as well as noise. Before the recognition step, all of the signals need to be preprocessed and the noisy parts need to be removed. So, Empirical Mode Decomposition (EMD) and normalization preprocessing steps are applied to the signals. Finally, the signals in the dataset are compared with Dynamic Time Warping for classification and accurate classification rate of 91.92% is obtained.