An Novel Approach to Predict and Classify the Mental State of Person using EEG-based Brain-Computer Interface

Abstract

A person’s present state of mind is determined by a complex collection of brain activities that make up their mental state. It is influenced by several internal and external aspects of the brain. By examining an individual’s EEG patterns, one can ascertain their mental state. In order to recognise and alter harmful or troubling thinking patterns that have a detrimental impact on behaviour and emotions, we classified three different states as: relaxed, neutral, and focused. To classify and predict the behaviour of a person based on certain mental states, we deployed popular machine learning models like k-NN, RF, XGBOOST, and EL to classify different mental states. Moreover, to predict the mental states, we implemented deep learning models like CNN, RNN, and LSTM. XGBoost achieves the highest classification accuracy (97.29%) with 5-fold cross validation. For the prediction, RNN achieved the highest prediction accuracy of 97.84%.