The Role of EEG-based Brain Computer Interface using Machine Learning Techniques: A Comparative Study

Abstract

A brain-computer interface (BCI) is a way for humans and machines to communicate directly. It has been used in many successful ways over the last few decades. A wide variety of fields, from economics to biology, make heavy use of machine and deep learning techniques these days. In general, these strategies may be employed in two ways: adapting well-known models and architectures to the given data or creating custom architectures. There are a lot of ways to speed up the research process, so it is important to know which types of models work best for a certain problem and/or data set. For the first time, a machine learning benchmark for EEG signal classification is presented in this paper. This review article takes a close look at the Brain-Computer Interface (BCI) and how it makes use of Machine Learning (ML) technology. It examines the many sorts of research in this area and explores the significance of ML in executing various BCI tasks. These include mental state detection, task categorization, categorization of emotional states, classification of electroencephalogram signals and the occurrence of event-related potentials (ERP), as well as motor imagery classification. A comparative assessment of the studied approaches is provided in this study, which investigates feature extraction, selection, and classification. BCI and ML advancements are discussed in this article, as well as the types of improvements that will be necessary in the future to produce better BCI applications.