ANALYSIS OF EEG SIGNAL FOR USING IN BIOMETRICAL CLASSIFICATION Roman Zak, Jaromir Svejda, Roman Senkerik and Roman Jasek Department of Informatics and Artificial Intelligence Tomas Bata University in Zlin Nam T.G. Masaryka 5555, 760 01 Zlin, Czech Republic. E-mail: {rzak, svejda, senkerik, jasek}@fai.utb.cz KEYWORDS Brain Computer Interface, EEG, Signal processing, Neural network classification, ABSTRACT Aim of this article is to clarify the potential use of EEG signal in modern information age. The basic principle of Brain Computer Interface (BCI) lies in the connection of brain waves with output device through some interface. BCI technology represents a communication interface between brain and computer. To sense electric signal from the brain, it is usually used an equipment based on the last results of scientific research on neuro- technology. Communication is provided by wireless transmission through which the signal is transmitted from the equipment to personal computer. Then the signal is analysed, processed and used for finding appropriate classification parameters. INTRODUCTION Many scientific disciplines deal with the human brain; for example numerical neuroscience, neuro-informatics, informatics or medicine. All of them bring theories, which could explain different brain activities. Numerical neuroscience provides mathematical and biophysical models, which are able to model basic processes in neurons and neural networks. The main goal of neuro-informatics is systematic development of database intended to collect information such as brain morphology, brain parts anatomy and their functional connection, brain electrophysiology, brain states obtained with magnetic resonance and their integration. Further, it seeks to develop tools for modelling, where the aim is the most accurate emulation of brain activity. In Informatics, complex networks are highly suitable to model a complex system among which the brain includes. The contribution of medicine is undisputable especially in brain anatomy research. The human brain is a complex system, which is an object of our research. It is regarded as the most complex system in the universe. The modern science is currently attempting to understand the complex interconnection among individual parts of the brain (Sporns et.al. 2005). There are many publications, which deal with description of the brain (Adeli 2010; Damasio 1995; Sporns et al. 2005). Currently there are many known applications of BCI technology, but not enough at each particular field of study. Signal that is sensed from the brain is the key element in the BCI model; therefore the design of an appropriate algorithm for processing of the signal is the most discussed part of BCI model structure (Schalk et al. 2004). Invasive methods of sensing the brain activity could provide very accurate data, but it is not both technically and user friendly; thus, it would not be further mentioned in this article. On the other hand, more accessible non - invasive methods can obtain relatively weak signal with amplitude ranging from units to hundreds of microvolts. Moreover, the signal is also prone to noise elements. Another disadvantage of this method is a summation of neuron signals; thus, obtained data are referenced to a specific group of neurons. The brain itself is composed of several parts, without which his activity could not be possible. One of its basic structural parts is a neuron. The neuronal cells are characterized by the fact that electrical activity is carried out in them. These cells communicate with each other by electrical signals. According to the last estimate, there are approximately 10 11 neurons in the brain. Every one of them is connected with thousands of other neurons. The main source of EEG signal is an electric activity of synapse - dendrites membrane located in the surface layer of the cortex. Each active synapse dispatches electromagnetic pulse to the environment during excitation. Due to the high number of these pulses, it is difficult to locate their source by means of multichannel sensor on the skin. This issue could be compared to full amphitheatre, in which there are chanting people and the task is to recognize from outside, which specific group of fans shouts. A different perspective on this issue may be such that the aim is to identify uniqueness of the signal for each individual subject. In the example shown above, it is as we would like to recognize the type of the stadium by the mass of chanting people. For example, there is noticeable difference between hockey and tennis fans. The biometric signatures are different for each creature on the planet Earth. METHODS There are several approaches for sensing brain activity. The most widely used is EEG technology, which Proceedings 28th European Conference on Modelling and Simulation ©ECMS Flaminio Squazzoni, Fabio Baronio, Claudia Archetti, Marco Castellani (Editors) ISBN: 978-0-9564944-8-1 / ISBN: 978-0-9564944-9-8 (CD)