TR Dizin İndeksli Yayınlar Koleksiyonu / TR Dizin Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/20.500.14365/4
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Article Citation - WoS: 11Citation - Scopus: 14Evaluation of Mother Wavelets on Steady-State Visually-Evoked Potentials for Triple-Command Brain-Computer Interfaces(Tubitak Scientific & Technical Research Council Turkey, 2021-09-23) Sayilgan, Ebru; Yuce, Yilmaz Kemal; Isler, YalcinWavelet transform (WT) is an important tool to analyze the time-frequency structure of a signal. The WT relies on a prototype signal that is called the mother wavelet. However, there is no single universal wavelet that fits all signals. Thus, the selection of mother wavelet function might be challenging to represent the signal to achieve the optimum performance. There are some studies to determine the optimal mother wavelet for other biomedical signals; however, there exists no evaluation for steady-state visually-evoked potentials (SSVEP) signals that becomes very popular among signals manipulated for brain-computer interfaces (BCIs) recently. This study aims to explore, if any, the mother wavelet that suits best to represent SSVEP signals for classification purposes in BCIs. In this study, three common wavelet-based features (variance, energy, and entropy) extracted from SSVEP signals for five distinct EEG frequency bands (delta, theta, alpha, beta, and gamma) were classified to determine three different user commands using six fundamental classifier algorithms. The study was repeated for six different commonly-used mother wavelet functions (haar, daubechies, symlet, coiflet, biorthogonal, and reverse biorthogonal). The best discrimination was obtained with an accuracy of 100% and the average of 75.85%. Besides, ensemble learner gives the highest accuracies for half of the trials. Haar wavelet had the best performance in representing SSVEP signals among other all mother wavelets adopted in this study. Concomitantly, all three features of energy, variance, and entropy should be used together since none of these features had superior classifier performance alone.Article Citation - WoS: 1Citation - Scopus: 1Humanoid Robot Arm Design, Simulation, Kinesthetic Learning, Impedance Control and Suggestions(Gazi Univ, Fac Engineering Architecture, 2022-02-28) Ertugrul, Seniz; Kaya, Ozan; Turkmen, Dila; Eraslan, Hulya; Taglioglu, Gokce Burak; Gulec, Musa OzgunRobot technology is constantly developing and the studies in this field are also increasing in our country. Universities, machine-manufacturing and defense industry have been either doing or planning robot projects. This study presents designing of a humanoid robot arm desired to be cooperative so that it can work as dual arm or with human operator. Mechanical design, kinematic and dynamic analysis, kinesthetic learning, impedance control, software and hardware studies were carried out within the scope of the study. The stages from the initial design of the humanoid robot arm to the control, the problems encountered, the experiences gained and the suggestions for advanced designs are shared in a very comprehensive way in this article. It has been explained in an easy-to-understand manner in order to be useful for national robot projects which are being developed especially for commercial purposes. Mechanical design, dynamic analyses, simulation and other files will be shared as open source with interested researchers.