Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/11
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Conference Object Dalgacık gürültü giderme yöntemiyle mikrodalga bileşen karakterizasyonunun iyileştirilmesi(IEEE, 2023) Karatay, Anıl; Olcay, Bilal Orkan; Yaman, FatihIn 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: 7Time-Resolved Eeg Signal Analysis for Motor Imagery Activity Recognition(Elsevier, 2023) Olcay, Bilal Orkan; Karaçalı, BilgeAccurately characterizing brain activity requires detailed feature analysis in the temporal, spatial, and spectral domains. While previous research has proposed various spatial and spectral feature extraction methods to distinguish between different cognitive tasks, temporal feature analysis for each separate brain region and frequency band has been largely overlooked. This study introduces two novel approaches for recognizing cognitive activity: temporal entropic profiling and time-aligned common spatio-spectral patterns analysis. These approaches capture and use discriminative short-lived signal segments for motor imagery activity recognition. In Approach-1, we evaluated nine different measures to determine timing parameters that showed altered behavior associated with maximal inter-activity differences, which we then used in a machine-learning framework. In Approach-2, we used the best-performing signal characteristic measures from Approach-1 to determine the optimum latency of each channel at each frequency band for a CSP-based activity recognition strategy. We evaluated both approaches on two online available motor imagery EEG datasets and achieved average recognition accuracy levels of 86%. We compared our methods with four established BCI methods. The performance results show that our approaches exceeded the benchmark methods' performances, with notable improvements in the proposed time-aligned common spatio-spectral patterns approach. This study demonstrates that motor imagery recognition performance is improved when a temporal analysis is adopted alongside spatio-spectral neural feature analysis and that timing parameters associated with the maximal entropic difference of EEG segments to the cognitive tasks varied between different brain regions and subjects. © 2023 Elsevier LtdArticle Citation - WoS: 14Citation - Scopus: 18Separating Normosmic and Anosmic Patients Based on Entropy Evaluation of Olfactory Event-Related Potentials(Elsevier Ltd., 2019) Güdücü, Çağdaş; Olcay, Bilal Orkan; Schaefer, L.; Aziz, M.; Schriever, V. A.; Özgören, Murat; Hummel, T.Objective: Methods based on electroencephalography (EEG) are used to evaluate brain responses to odors which is challenging due to the relatively low signal-to-noise ratio. This is especially difficult in patients with olfactory loss. In the present study, we aim to establish a method to separate functionally anosmic and normosmic individuals by means of recordings of olfactory event-related potentials (OERP) using an automated tool. Therefore, Shannon entropy was adopted to examine the complexity of the averaged electrophysiological responses. Methods: A total of 102 participants received 60 rose-like odorous stimuli at an inter-stimulus interval of 10 s. Olfactory-related brain activity was investigated within three time-windows of equal length; pre-, during-, and post-stimulus. Results: Based on entropy analysis, patients were correctly diagnosed for anosmia with a 75% success rate. Conclusion: This novel approach can be expected to help clinicians to identify patients with anosmia or patients with early symptoms of neurodegenerative disorders. Significance: There is no automated diagnostic tool for anosmic and normosmic patients using OERP. However, detectability of OERP in patients with functional anosmia has been reported to be in the range of 50%.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; Karaçalı, BilgeDuring 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.Conference Object Citation - WoS: 1Citation - Scopus: 1Deneysel Mod Ayrıştırması Uygulanmış Yazma Hareket Bilgisi Kullanılarak El Yazısı Karakter Tanıma(IEEE, 2017) Tuncer, Esra; Olcay, Bilal Orkan; Unlu, Mehmet ZubeyirIn 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.Conference Object Citation - Scopus: 3Entropik Kümeleme Kullanılarak Beyin Aktivitesi Karakterizasyonu(IEEE, 2017) Olcay, Bilal Orkan; Karacali, Bilge; Ozgoren, Murat; Guducu, CagdaIn 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.Conference 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; Karaçalı, BilgeBu 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.