A study of classification techniques on P300 speller dataset

Communication is the basic need of humans to interact with their surroundings. BCI acts as a great tool for providing the means of basic communication to people with locked-in syndrome. For such patients, these basic things matter most and give them a sense of independence. Communication through BCI can be done using various paradigms, P300 speller is one of these paradigms in which the user is instructed to focus their attention on the desired character and able to produce negative and positive slow cortical potential changes which is then interpreted by the BCI system. Although P300 based BCI was introduced over twenty years ago, the past few years have seen a rapid increase in P300 based BCI research. In this paper, we overview the current status of P300 BCI technology and also discussed and compare different approaches using Random Forest, Support Vector Machine (SVM), and Extreme Gradient Boosting (XgBoost) classification algorithm.