L. Alvarez et al. (Eds.): CIARP 2012, LNCS 7441, pp. 682–690, 2012. © Springer-Verlag Berlin Heidelberg 2012 Recognition and Real-Time Detection of Blinking Eyes on Electroencephalographic Signals Using Wavelet Transform Renato Salinas 1 , Enzo Schachter 1 , and Michael Miranda 2 1 Departamento de Ingeniería Mecánica 2 Programa de Doctorado en Automatización Facultad de Ingeniería, Universidad de Santiago de Chile, Santiago, Chile {renato.salinas,enzo.schachter,michael.mirandas}@usach.cl Abstract. In this paper we study the detection of a specific pattern associated with the blinking of an eye in real time using electroencephalogram (EEG) signals of a single channel. This paper takes into account the theoretical and practical principles enabling the design and implementation of a system for real-time detection of time location, regardless of scale and multiple incidences. By using wavelet transform it permits us the fulfillment of our objective. The multiple detection and real-time operation is achieved by working with a pop- up window giving the projection of an ongoing analysis of the signal sampled by the EEG. Keywords: biological signals, electroencephalogram EEG, brain computer in- terface BCI, eye blink detection, pattern recognition, wavelet transform. 1 Introduction The electroencephalogram or EEG was first used in humans by Hans Berger in 1924 [1], with the purpose of recording electric potentials of the brain. These signals are acquired from sensors called electrodes attached to the scalp of the subject. The func- tion of an electrode is to passively collect electrical potentials from neuron banks that are located mainly in the cerebral cortex. The level of these signals is typically within the range of 40 to 100 microvolts [2]. Given their low electrical levels, EEG signals can be easily contaminated by other sources. An EEG signal that does not originate in the brain is called an artifact. The artifacts fall into two categories: physiological and non-physiological. Any source in the body that has an electric dipole generates an electric field capable of producing physiological artifacts. The non-physiological artifacts are produced by electrical and mechanical devices [3]. The human eye, simi- lar to an electrical system, acts as a dipole with a positive charge in front and a nega- tive charge in the back; the exercise of closing and opening the eyes produces artifacts of EEG signals [4]. Given that the artifacts are usually considered an unwanted signal or signal interference, this work is focused on real-time detection of a specific pattern generated by the blink of an eye so that they can be removed. The artifact generated by the blink of an eye is not necessarily a problem, but an opportunity, because its