One Approach to using R for Bayesian Analysis of Brain Signals

The study of brain signals based on efficient computing is a new beginning in the research field aimed at finding a connection between human emotions and recorded BCI signals. Analysis of incoming brain signals and techniques for processing and classifying information are actively researched. Investigating these signals is a complex process that requires multidisciplinary skills and knowledge in computer science, signal processing, neuroscience, robotics, artificial intelligence, and more. This research focuses on the classification of brain-computer interface (BCI) data using Bayesian analysis. The aim of the paper is to study the classification of brain signals and the possibilities of reducing the number of channels. Experimental data were obtained using Emotiv Epoc 14+. The R programming language was used to process the data. The data were classified using Bayesian analysis in R.