A Prefatory Analysis of Brain Computer Interfacing Based on EEG

Abstract

An extremely developing area of application systems science is defined by brain programming interface technology. In health fields, its contributions range from treatment to synaptic healing for severe injuries. The special fingerprint of mind reading and remote contact in several areas, such as education, self-regulation, manufacturing, marketing, protection, entertainment, and games. It induces shared trust between consumers and systems around them. Deep learning has already received mainstream recognition and has been used in numerous applications, like natural language processing (NLP), computer vision, and voice. For MI EEG signal classification, however, deep learning has seldom been used. This paper highlights the fields of application that could advantage from brain waves in promoting or attaining their objectives. We also answer big usableness and technological problems facing the use of brain signals in different BCI device components. Various solutions aimed at minimizing and reducing their effects have also been studied. The popular spatial pattern (CSP) approach, which is generally utilized, is applied to extract variance-based CSP functions, that are then fed for classification to DNN. DNN practice has been thoroughly studied for classification of MI-BCI and best framework found has been explored.