Browsing by Author "Karakullukcu, Nedime"

Now showing 1 - 3 of 3

Citation - WoS: 11
Citation - Scopus: 14
Detection of Movement Intention in EEG-Based Brain-Computer Interfaces Using Fourier-Based Synchrosqueezing Transform
(World Scientific Publ Co Pte Ltd, 2021) Karakullukcu, Nedime; Yilmaz, Bulent; 01. Abdullah Gül University
Patients with motor impairments need caregivers' help to initiate the operation of brain-computer interfaces (BCI). This study aims to identify and characterize movement intention using multichannel electroencephalography (EEG) signals as a means to initiate BCI systems without extra accessories/methodologies. We propose to discriminate the resting and motor imagery (MI) states with high accuracy using Fourier-based synchrosqueezing transform (FSST) as a feature extractor. FSST has been investigated and compared with other popular approaches in 28 healthy subjects for a total of 6657 trials. The accuracy and f-measure values were obtained as 99.8% and 0.99, respectively, when FSST was used as the feature extractor and singular value decomposition (SVD) as the feature selection method and support vector machines as the classifier. Moreover, this study investigated the use of data that contain certain amount of noise without any preprocessing in addition to the clean counterparts. Furthermore, the statistical analysis of EEG channels with the best discrimination (of resting and MI states) characteristics demonstrated that F4-Fz-C3-Cz-C4-Pz channels and several statistical features had statistical significance levels, p, less than 0.05. This study showed that the preparation of the movement can be detected in real-time employing FSST-SVD combination and several channels with minimal pre-processing effort.
El Protezleri için EEG ve EMG Sinyalleriyle Algı Kestirimi ve Tork Kontrolü
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Karakullukcu, Nedime; Yılmaz, Bülent; 01. Abdullah Gül University
Upper extremity prostheses vary based on patients' articulation levels and movement methods. They can be cosmetic, operate mechanically with shoulder movement, or be controlled by myoelectronic and electroencephalography (EEG) signals. However, unnatural prosthesis control burdens users mentally. This thesis seeks to enhance bionic hand prosthesis control using EEG and electromyography (EMG) signals, coupled with users' visual weight perception, aiming to reduce physical and mental discomfort associated with mechanical prostheses. The prototype hand's preconditioning evaluates objects' weight visually, aiming to reduce shoulder force and mental load while holding an object. EEG and EMG signals from subjects were processed for real-time implementation. In the first stage, a study focused on operating the prosthesis using the motor intention waves of prosthesis users, and the machine learning approaches' classification success (detection of the intention to activate the prosthesis) was examined using EEG data from 30 healthy participants. The second stage recorded EEG and EMG signals from 31 participants during reaching, lifting, and placing an object, employing various classifications for object weight. In the real-time classification of multi-channel EEG signals from 20 healthy individuals using Fourier-based synchrosequeezing transform (FSST) and singular value decomposition (SVD) approaches, the system aimed to control the stiffness of the wrist part of the prosthesis. Consequently, the system could detect the weight of the object perceived by the user while using the prosthesis, allowing for the preconditioning of the prosthesis based on this weight when the user wants to hold and move the object.
Citation - WoS: 2
Citation - Scopus: 3
Object 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, Bulent; 01. Abdullah Gül University; 02. Mühendislik Fakültesi; 02.05. Elektrik & Elektronik Mühendisliği
This 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.