Investigation of the Effects of Statistically Significant Features on the Classification of EEG-Based Motor Imagery Tasks

Murside Degirmenci

Motor imagery (MI) task classification is highly prevalent in Electroencephalography (EEG)-based Brain-Computer Interface (BCI) research area. Extremity movement task classification and finger movement classification studies are presented in this thesis. In extremity movement classification, binary-class (right hand and left hand) and multi-class (right hand, left hand, right hand, and left hand) classifications are performed using 4 different feature extraction approaches and statistically significance-based feature selection (the independent t-test, one-way ANOVA test). Firstly, time- ...Daha fazlası

Doktora Tezi 2024 Yalçın İşler İzmir Katip Çelebi Üniversitesi Fen Bilimleri Enstitüsü 6 Görüntülenme

Erişime Açık

Evaluating steady-state visually-evoked potentials using ensemble learning methods Durağan hal görsel uyarılmış potansiyellerin topluluk öğrenmesi yöntemleriyle değerlendirilmesi

Sayılgan, Ebru

ÖZETSteady-state visual evoked potentials (SSVEPs) have been designated to be appropriate and useful for many areas in clinical neuroscience, cognitive, and in engineering. SSVEPs have become popular recently, due to their advantages such as high bit rate, simple system structure, and short training time, etc. To design SSVEP based BCI system, signal processing methods appropriate to the signal structure should be applied. One of the most appropriate signal processing methods of these non-stationary signals is the Wavelet Transform. After literature searched, we noticed that there was no study ...Daha fazlası

Doktora Tezi 2020 Yalçın İşler Fen Bilimleri Enstitüsü 39 Görüntülenme