Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Conference Object Citation - Scopus: 2Sliding Window and Filterbank Utilization on Riemannian Geometry(Institute of Electrical and Electronics Engineers Inc., 2022-08-08) Altindis, Fatih; Yilmaz, BulentRiemannian geometry-based signal processing approaches on EEG signals provides similar decoding performance compared to state-of-the-art methods. However, Riemannian geometry framework requires predefine EEG signal epoch that is to be used in the analysis. Sliding window approach that operates in Riemannian geometry proposed to enable use of EEG signals without constrained by the record length. Decoding performance of tangent space mapping was increased more than 6% in overall accuracy compared the previous study's results. Instead of using single band-pass filter, utilization of filterbank is proposed to increase decoding performance. Distance based Riemannian classifier's overall performance were increased by 5% compared to standard Riemannian geometry approach. © 2022 Elsevier B.V., All rights reserved.Article Citation - Scopus: 1Robust Controller Electromyogram Prosthetic Hand With Artificial Neural Network Control and Position(Indian Journal of Forensic Medicine and Toxicology ijfmt@hotmail.com, 2020) Ahmed, Saygin Siddiq; Ahmed, Aydin S.; Yilmaz, Bulent; Doǧru, NuranIn this study, we proposed and designed a new control method for an electromyographically (EMG) controlled prosthetic hand. The objective is to increase the control efficiency of the human–machine interface and afford greater control of the prosthetic hand. The process works as follows: EMG biomedical signals acquired from Myoware sensors positioned on the relevant muscles are sent to the robot that consist of hand, Arduino and MATLAB program, which computes and controls the hand position in free space along with hand grasping operations. The Myoware device acquires muscle signals and sends them to the Arduino. The Arduino analyzes the received signals, based on which it controls the motor movement. In this design, the muscle signals are read and saved in a MATLAB system file. After program processing on the industrial hand which is applied by MATLAB simulation, the corresponding movement is transferred to the hand, enabling movements, such as, hand opening and closing according to the signal stored in the MATLAB system. In this study, hand and fingerprints were designed using a three-dimensional printer by separate recording finger and thumb signals. The muscle signals were then analyzed in order to obtain peak signal points and convert them into data. These results indicate the effectiveness of the proposed method and demonstrate the superiority of the method for amputees because of the improved controllability and perceptibility afforded by the design. © 2020 Elsevier B.V., All rights reserved.Conference Object Citation - WoS: 1Citation - Scopus: 1Polyp Localization in Colonoscopy Images Using Vessel Density(Institute of Electrical and Electronics Engineers Inc., 2018-11) Doǧan, Refika Sultan; Yilmaz, BulentIn this paper, we present a new approach for polyp localization in colonoscopy images. This approach is based on the determination of the polyp location using the vessel density in colon images. Primarily, we used pre-processing procedures on the colon images, and then blood vessel extraction techniques were employed. Later, segmentation of the vessel boundaries was performed. With the help of vessel boundaries we calculated the vessel density, and used this for the localization of the polyps. We tested the success of this approach using a publicly available image set (CVC-ClinicDB database). This database consisted of 612 images from 29 different polyps. This approach succeeds in correct detection of 24 out of 29 different polyps. © 2019 Elsevier B.V., All rights reserved.Conference Object Multivariate Pseudo Wigner Ville Distribution Based Emotion Detection From Electrical Activity of Brain(IEEE, 2017) Ozel, Pinar; Akan, Aydin; Yilmaz, BulentRecently, there has been a rapid development in multivariate signal analysis to determine joint oscillations for multiple data channels. The emotion elicitation in an electroencephalogram (EEG) is a novel area to evaluate methods for emotional differences from brain signals. In this paper, utilizing the idea of joint instantaneous frequency of multivariate data, a multivariate extension of pseudo Wigner distribution is used for emotion recognition from EEG signals, in which different window sizes are employed to interpret the results. As a preliminary study, the best results are obtained as 90%, 75%, 65% in terms of valence, arousal and dominance scale respectively for larger window size.Conference Object Citation - Scopus: 1Many-to Transfer Learning on Motor Imagery BCI(Institute of Electrical and Electronics Engineers Inc., 2024-12-11) Altindis, Fatih; Yilmaz, Bulent; Congedo, MarcoThis paper presents many-to-many domain adaptation strategy, named group learning, for motor imagery brain-computer interfaces (BCIs). Group learning, grounded in Riemannian geometry, simultaneously aligns multiple domains in a unified model, whereas fast alignment approach integrates new, unseen domains without re-estimating alignment matrices for all domains. Group learning creates a single machine learning model using data from previous subjects and/or sessions. Fast alignment utilizes the already trained model for an unseen domain without requiring any additional classifier training. The tests on five publicly available motor imagery databases demonstrate the robustness of group learning against negative learning. The classification accuracy scores of binary and multiclass databases show comparable, if not superior, performance to conventional subject-wise learning method. © 2025 Elsevier B.V., All rights reserved.Conference Object Citation - WoS: 3Citation - Scopus: 3Examining Tongue Movement Intentions in EEG With Machine and Deep Learning: An Approach for Dysphagia Rehabilitation(IEEE, 2024-08-26) Aslan, Sevgi Gokce; Yilmaz, BulentDysphagia, a common swallowing disorder particularly prevalent among older adults and often associated with neurological conditions, significantly affects individuals' quality of life by negatively impacting their eating habits, physical health, and social interactions. This study investigates the potential of brain-computer interface (BCI) technologies in dysphagia rehabilitation, focusing specifically on motor imagery paradigms based on EEG signals and integration with machine learning and deep learning methods for tongue movement. Traditional machine learning classifiers, such as K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, Naive Bayes, Random Forest, AdaBoost, Bagging, Kernel, and Neural Network were employed in discrimination of rest and imagination phases of EEG signals obtained from 30 healthy subjects. Scalogram images obtained using continuous wavelet transform of EEG signals corresponding to the rest and imagination phases of the experiment were used as the input images to the CNN architecture. As a result, KNN and SVM, exhibited lower accuracy rates compared to ensemble methods like AdaBoost and Random Forest, which are effective in handling complex datasets. Additionally, a deep learning approach achieved an accuracy rate of 83%. Overall, this study demonstrates the promising role of BCI technologies and machine learning techniques in dysphagia rehabilitation.Conference Object Citation - WoS: 1Citation - Scopus: 3Emotional State Analysis From EEG Signals via Noise-Assisted Multivariate Empirical Mode Decomposition Method(IEEE, 2017) Ozel, Pinar; Akan, Aydin; Yilmaz, BulentEmotional state analysis is an interdisciplinary arena because of the many parameters that encompass the complex neural structure and electrical signals of the brain and in terms of emotional state differences. In recent years, emotional state data have been examined by using data-driven methods such as Empirical Mode Decomposition as well as classical time-frequency methods. Although Empirical Mode Decomposition has many advantages, it has disadvantages such as being designed for univariate data, prone to mode mixing, and providing signal via a sufficient number of the local extrema. To overcome these disadvantages, in this study, the Noise-Assisted Multivariate Empirical Mode Decomposition has been shown to classify the emotional state using electroencephalographic signals.Conference Object Citation - Scopus: 1Detection of Epileptic Seizures With Tangent Space Mapping Features of EEG Signals(IEEE, 2021-11-04) Altindis, Fatih; Yilmaz, BulentDetection of epileptic seizures from EEG signals is well-studied topic for the last couple of decades. Lately, automated signal processing and machine learning methods were developed to detect epileptic seizures. However, most of the methods are tailored to subjects and require fine tuning of many parameters. In this study, we proposed to use Riemannian geometry-based signal processing method that already showed superior performance on brain-computer interface problems, to extract features. We showed that tangent space mapping features of EEG signals can be used to detect seizures with high accuracy and precision.Conference Object Akciğer Tümörlü Hastaların PET ve BT Görüntülerinin Çakıştırılıp Birleştirilmesi(IEEE, 2015-10) Ayyildiz, Oguzhan; Yilmaz, Bulent; Karacavus, Seyhan; Kayaalti, Omer; Icer, Semra; Eset, Kubra; Kaya, EserImage fusion attracts attention in medical field due to complementary behavior and application such as diagnosis and treatment planning. In this study, first positron emission tomography (PET) and computed tomography (CT) images coming from 8 nonsmall cell lung cancer were registered then wavelet and principal component analysis methods were applied to fuse images. According to mutual information metric and nuclear medicine expert wavelet method gave better results when compared to PCA.Conference Object Citation - Scopus: 2Beyin Dalgalari ve Baş Hareketiyle Gerçek Zamanli Robotik Araba Kontrolü(Institute of Electrical and Electronics Engineers Inc., 2018-11) Oztürk, Nedime; Yilmaz, Bulent; Onver, Ahmet YasinEmotiv Epoc Headset is a portable and low-cost device. In this study, Emotiv Epoc headset was used in order to obtain real-time gyro and EEG signals. The aim of this study was to control a robotic car in real-time by using head movement and opening and closing of the eyes. The maximum and minimum amplitude of the gyro signal, and the ratios of the beta waves of O1 and O2 channel to alpha waves of the same channels were used as threshold values. These threshold values were used to determine the direction of the robotic car. Because of its low-cost and easy implementation, Arduino Uno was used to manage the robotic car. This study has shown that brain waves and head movements can control a device in real time. This system has the potential to be used in neurofeedback and brain-computer interface applications. © 2019 Elsevier B.V., All rights reserved.
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