Analysis of Brain Computer Interface Using Deep and Machine Learning

Abstract

Pattern recognition is becoming increasingly important topic in all sectors of society. From the optimization of processes in the industry to the detection and diagnosis of diseases in medicine. Brain-computer interfaces are introduced in this chapter. Systems capable of analyzing brain signal patterns, processing and interpreting them through machine and deep learning algorithms. In this chapter, a hybrid deep/machine learning ensemble system for brain pattern recognition is proposed. It is capable to recognize patterns and translate the decisions to BCI systems. For this, a public database (Physionet) with data of motor tasks and mental tasks is used. The development of this chapter consists of a brief summary of the state of the art, the presentation of the model together with some results and some promising conclusions.