Thesis (M.Eng.). / Written for the Dept. of Biomedical Engineering. Title from title page of PDF (viewed 2008/07/29). Includes bibliographical references.

This thesis study focuses on the realization of an online cue based Motor Imagery (MI) Brain Computer Interface (BCI). For this purpose, some signal processing and classification methods are investigated. Specifically, several time-spatial-frequency methods, namely the Short Time Fourier Transform (STFT), Common Spatial Frequency Patterns (CSFP) and the Morlet Transform (MT) are implemented on a 2-class MI BCI system. Distinction Sensitive Learning Vector Quantization (DSLVQ) method is used as a feature selection method. The performance of these methodologies is evaluated with the linear and nonlinear Support Vector Machines (SVM), Multilayer Perceptron (MLP) and Naive Bayesian (NB) classifiers. The methodologies are tested on BCI Competition IV dataset IIb and an average kappa value of 0.45 is obtained on the dataset. According to the classification results, the algorithms presented here obtain the 4th level in the competition as compared to the other algorithms in the competition. Offline experiments are performed in METU Brain Research Laboratories and Hacettepe Biophysics Department on two subjects with the original cue-based MI BCI paradigm. Average prediction accuracy of the methods on a 2-class BCI is evaluated to be 76.26% in these datasets. Furthermore, two online BCI applications are developed: the ping-pong game and the electrical wheelchair control. For these applications, average classification accuracy is found to be 70%. During the offline experiments, the performance of the developed system is observed to be highly dependent on the subject training and experience. According to the results, the EEG channels P3 and P4, which are considered to be irrelevant with the motor imagination, provided the best classification performance on the offline experiments. Regarding the observations on the experiments, this process is related to the stimulation mechanism in the cue based applications and consequent visual evoking effects on the subjects.

Taking research as a tool to learn how new technology can develop new diagnosis and treatment methods in the physical field, takes place the education in motor sciences. On one hand, current research has shed light into novel methods to improve motor performance for athletes as well as for people learning new motor gestures. On the other hand it has also helped to improve treatment efficiency for people suffering motor cerebral lesions like: cerebrovascular attack (CVA) and cerebral palsy. This doctoral thesis addresses different protocols to analyze motor gestures and brain oscillations through visual perception.Our brain encompasses a changing symphony of oscillating activity throughout our lives. Up to the time we are born, we are ready to feel and move to interact with our world. Our senses develop rapidly and we start to perceive the world and learn. We visually perceive and process big amounts of information on a daily basis. At the same time we see movements from ourselves and from others in order to communicate and interact with our environment. We watch the world move. Moreover, from the links that exist between motor and sensory systems in human beings we may approach individual motor activity as a loop between a control (brain) over the effectors (muscles) which act, perceive and send the information back to the control source.The present group of works presented in this doctoral thesis is based on the correlation between human brain scalp activity, measured by means of electroencephalography (EEG) recordings, visual perception and its interpretation through different approaches. / Doctorat en Sciences de la motricité / info:eu-repo/semantics/nonPublished

Title of thesis: Source analysis and comparation of Feldenkrais inspired movement and visual stimulation using sLORETA Objectives: The thesis aim is to evaluate intracerebral source activity during a simple arm movement inspired by Feldenkrais method and to compare it with a visual stimulation of the same movement presented in a clip and with an imagination of the same movement. The movement inspired by Feldenkrais method was simplified to a repeated flexion of the dominant arm. Source analysis was evaluated from EEG and processed using sLORETA program, Methods: To obtain the data, experimental group was put together containing 12 participants aged 22-60, (mean = 27.2), both genders included. Participants were subjected to one-off measurement by the EEG instrument. Feldenkrais inspired movement of a flexion of a dominant upper right arm was investigated. The experiment constisted of six parts: 1. native EEG record with eyes closed and open, 2. active flexion of the dominant upper arm with eyes closed, 3. active flexion of the dominant upper arm with eyes opened, 4. watching video presenting repeated upper arm flexion, 5. dominant upper arm flexion imagination with eyes closed. Every part lasted for two minutes. Between individuals parts was inserted a pause. Obtained EEG data were processed with...