EEG Signal Processing for BCI ApplicationsAvid Roman-GonzalezTo cite this version:Avid Roman-Gonzalez.EEG Signal Processing for BCI Applications.Human - ComputerSystems Interaction: Backgrounds and Applications 2, Advances in Intelligent and Soft Com-puting, 2012, 98 (1), pp.51-591.<10.1007/978-3-642-23187-236>.<hal-00742211>HAL Id: hal-00742211Submitted on 16 Oct 2012HALisamulti-disciplinaryopenaccessarchive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not.The documents may come fromteaching and research institutions in France orabroad, or from public or private research centers.L’archive ouverte pluridisciplinaireHAL, estdestin´ee au d´epˆot et `a la diffusion de documentsscientifiques de niveau recherche, publi´es ou non,´emanant des ´etablissements d’enseignement et derecherche fran¸cais ou ´etrangers, des laboratoirespublics ou priv´es.
EEG Signal Processing for BCI Applications A. Roman-Gonzalez11Department of Electronics Engineering, Universidad Nacional San Antonio Abad del Cusco, Peru, email@example.comAbstract.In this article we offer a communication system to people who undergo a severe loss of motor function as a result of various accidents and/or diseases so that they can control and interact better with the environment, for which a brain-computer interface has been implemented through the acquisition of EEG signals by electrodes and implementation of algorithms to extract characteristics and ex-ecute a method of classification that would interpret these signals and execute cor-responding actions The first objective is to design and construct a system of com-munication and control based on the thought, able to catch and measure EEG signals. The second objective is to implement the system of data acquisition in-cluding a digital filter in real time that allows us to eliminate the noise. The third objective is to analyze the variation of the EEG signals in front of the different tasks under study and of implementing an algorithm of extraction of characteris-tics. The fourth objective is to work on the basis of the characteristics of the EEG signals, to implement a classification system that can discriminate between the two tasks under study on the basis of the corresponding battles. 1 Introduction The work presented in this paper is based on [Roman-Gonzalez 2010 (1)] and [Roman-Gonzalez 2010 (2)]. There are a significant number of people suffering from severe motor disabilities due to various causes, high cervical injuries, cere-bral palsy, multiple sclerosis or muscular dystrophy. In these cases the communi-cation systems based on brain activity play an important role and provide a new form of communication and control, either to increase the integration into the so-ciety or to provide to these people a tools for interaction with their environment without a continued assistance. There are various techniques and paradigms in the implementation of brain-computer interfaces (BCI). A brain-computer interface is