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: 3Citation - Scopus: 4Object Weight Perception in Motor Imagery Using Fourier-Based Synchrosqueezing Transform and Regularized Common Spatial Patterns(IEEE-Inst Electrical Electronics Engineers Inc, 2024) Karakullukcu, Nedime; Altindis, Fatih; Yilmaz, BulentThis study addresses the challenge faced by individuals with upper-limb prostheses in regulating grip force and adapting movements to different object weights. Despite limited exploration, this research pioneers the use of EEG to estimate object weight perception in the context of upper-limb prostheses. Investigating neural correlates in this population provides valuable insights and aids the development of neurofeedback-based strategies for weight perception. Our objective is to identify EEG features predicting the weight perception of held objects. Employing Fourier-based synchrosqueezing transform (FSST) and regularized Common Spatial Patterns (CSP) features, we classify motor imagery waves representing three weight categories (light, medium, heavy). Subjects perform actual motor tasks before imagery sessions, and our approach integrates EEG features of both movements to train subject-specific machine learning models. Results reveal that FSST- singular value decomposition (SVD) features for medium and heavy objects are most distinctive. Achieving up to 90% accuracy, spatial features demonstrate effective classification of motor imagery for different weights. Unlike weight prediction studies, our focus is on visual perception and imagination of object weights, enhancing prosthetic hand system preconditioning. Binary classification surpasses 70% accuracy in predicting object weights, uniquely utilizing actual movement data for CSP algorithm regularization coefficient estimation.Conference Object Citation - Scopus: 2Many-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.