Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Conference Object Citation - WoS: 7Citation - Scopus: 12Use of Topological Data Analysis in Motor Intention Based Brain-Computer Interfaces(European Signal Processing Conference, EUSIPCO, 2018-09) Altindis, Fatih; Yilmaz, Bulent; İçöz, Kutay; Borisenok, S.This study aims to investigate the use of topological data analysis in electroencephalography (EEG) based on brain-computer interface (BCI) applications. Our study focused on extracting topological features of EEG signals obtained from the motor cortex area of the brain. EEG signals from 8 subjects were used for forming data point clouds with a real-time simulation scenario and then each cloud was processed with JPlex toolbox in order to find out corresponding Betti numbers. These numbers represent the topological structure of the point data cloud related to the persistent homologies, which differ for different motor activity tasks. The estimated Betti numbers has been used as features in k-NN classifier to discriminate left or right hand motor intentions. © 2019 Elsevier B.V., All rights reserved.Article Citation - WoS: 7Citation - Scopus: 9Split-Attention Effects in Multimedia Learning Environments: Eye-Tracking and EEG Analysis(Springer, 2022-02-02) Mutlu-Bayraktar, Duygu; Ozel, Pinar; Altindis, Fatih; Yilmaz, BulentThis study aimed to evaluate the split-attention effect in multimedia learning environments via objective measurements as EEG and eye-tracking. Two different multimedia learning environments in a focused (integrated) and split-attention (separated) format were designed. The experimental design method was used. The participants consisted of 44 students divided into two groups for focused attention and split-attention. There were significant differences between the fixation, brain wave, and retention performance of the two groups. Fixations of the split-attention group were higher than the focused attention group. A significant difference was found in the focused attention group in the alpha brain wave in the frontal region for intra-group comparisons and in the split-attention group in the beta brain wave in the frontal area for the inter-group comparison. The retention performance of the focused attention group was higher than the split-attention group. Accordingly, more cognitive activity emerged in environments where the text was not integrated into the picture. Additionally, the narration of text instead of printed text is effective for focusing attention. To prevent the emergence of a split-attention effect, the text should be integrated into the picture in designs. Due to the split-attention effect, the eye-tracking and EEG data were different between the groups.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.Article Citation - Scopus: 2Improving Short-Term Memory Performance of Healthy Young Males Using Alpha Band Neurofeedback(International Society for Neurofeedback and Research, 2019-03-24) Gökşin, Barış; Yilmaz, Bulent; İçöz, KutayTo examine whether it was possible to improve short-term memory performance of healthy participants by increasing relative alpha band power (7–11.5 Hz) using neurofeedback, we first converted a commercial EEG device (EmotivEpoc) to a neurofeedback tool and collected data from 11 healthy Turkish male graduate students in five neurofeedback sessions. Before and after neurofeedback training, a memorization task using 10 English words and their Turkish meanings was applied to all participants. The results indicated that 6 out of 11 participants were able to enhance their relative alpha band power with respect to other bands in the frequency spectrum during neurofeedback sessions. Although there was no obvious improvement in their short-term memory performance, we may conclude that neurofeedback training was beneficial for the participants to focus their minds consciously. However, it is not easy to mention that neurofeedback training certainly improved or was irrelevant with short-term memory performance. This study is important in the sense that for such a focused group the use of a commercial, customized low-cost EEG device was shown to be feasible for neurofeedback training sessions. © 2019 Elsevier B.V., All rights reserved.Article Citation - Scopus: 5Hyperplastic and Tubular Polyp Classification Using Machine Learning and Feature Selection(Elsevier B.V., 2024) Doǧan, Refika Sultan; Akay, Ebru; Doǧan, Serkan; Yilmaz, BulentPurpose: The aim of this study is to develop an effective approach for differentiating between hyperplastic and tubular adenoma colon polyps, which is one of the most difficult tasks in colonoscopy procedures. The main research challenge is how to improve the classification of these polyp subtypes applying various focusing levels on the polyp images, data preprocessing approaches, and classification algorithms. Methods: This study employed 202 colonoscopy videos from a total of 201 patients, focusing on 59 videos containing hyperplastic and tubular adenoma polyps. Manually extract key frames and several feature extraction and classification techniques were applied. The influence of different datasets with various focuses as well as data preprocessing steps on the performance of classification was examined, and AUC values were calculated using ten classifiers. Results: The study discovered that the optimal dataset, data preprocessing method, and classification algorithm all had significant effects on classification results. The Random Forest model with the Recursive Feature Elimination (RFE) feature selection approach, for example, consistently outperformed other models and achieved the highest AUC value of 0.9067. In terms of accuracy, F1 score, recall, and AUC, the suggested model outperformed a gastroenterologist, nevertheless precision remained slightly lower. Conclusion: This study emphasizes the importance of dataset selection, data preprocessing, and feature selection in enhancing the classification of difficult colon polyp subtypes. The suggested model offers a promising model for the clinical differentiation of hyperplastic and tubular adenoma polyps, potentially improving diagnostic accuracy in gastroenterology. © 2024 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.
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