Device control with eeg signals

Electroencephalography (EEG), Positron Emission Tomography (PET), Single Photon Emission Computed Tomography (TPEBT), Magnetoencephalography (MEG) and Functional Magnetic Resonance Imaging (fMRI) techniques are used in the measurement and analysis of electrical activities in the brain. However, the most reliable brain signal measurement and analysis technique has been accepted as EEG in terms of cost, size and risk of damage to the body. EEG signals are one of the most basic methods used to analyze brain activity. EEG is used as a test tool to evaluate the electrical activity in the brain. In 1929, for the first time by Hans Berger, brain activities were measured by introducing human EEG. Nearly a century ago, EEG was used as a diagnostic tool. It was also used in systems called Brain-Computer Interface (BBA). Neurologists have been able to diagnose the shape of a patient's brain wave activity and have found abnormalities that lead to epilepsy, seizures or other neurological disorders. With the development of the BBA, the quality of life of individuals with partial disabilities and health problems in the musculoskeletal system has been increased. Patients with paralysis, especially those who have lost their mobility, have increased the possibilities of survival and have developed systems to assist them. Today, many scientists are working on this subject. Many methods of EEG-based BBA are available in the literature. These methods are the most commonly used visual stimulus based methods when multiple classes and accuracy rates are considered in real-time BBAs. In this thesis, Steady State Visually Evoked Potential(SSVEP) based EEG signal analysis was performed. In accordance with the aim, the individual is asked to look at three different directions on the screen, left (6.66 Hz), right (8.57 Hz) and up (12 Hz), which oscillate at certain frequencies. When looking at the flickering shapes on the screen, the EEG signals were received with the Emotiv EpoC + device and the signals were analyzed and signals were analyzed in OpenVibe and Matlab software platform. OpenVibe also trained the system network using personal EEG signaling. Then, this network was trained and the system was tested online with real-time EEG signals. The EEG signals received in the Matlab software platform were passed through a bandpass filter and then subjected to Hilbert and Short Time Fourier Transform operations. Visually stimulated in the occipital region the SSVEP response was made to find out for every shape that Subject look at in different frekans. After the SSVEP response was detected, data collected from eleven subjects by Emotiv Epoc+ device by Multimedia Authoring and Management using your Eyes and Mind (MAMEM) organization for training were used for training with Artificial Neural Networks (ANN) and Support Vector Machines (SVM). Emotiv Epoc+ device and personal EEG data were used to test the trained classifier network. In this way, the direction in which the individual looked on the screen was determined using brain signals, and the Lego Mindstorm EV3 robot was controlled according to these directions. Successful testing of the results is expected in the future to integrate the system with nanotechnologies and mobile devices, which will make the device control in EEG based BBA workings much more precise, faster and more successful.