A Study on Mental State Classification using EEG-based Brain-Machine Interface

This work aims to find discriminative EEG-based features and appropriate classification methods that can categorise brainwave patterns based on their level of activity or frequency for mental state recognition useful for human-machine interaction. By using the Muse headband with four EEG sensors (TP9, AF7, AF8, TP10), we categorised three possible states such as relaxing, neutral and concentrating based on a few states of mind defined by cognitive behavioural studies. We have created a dataset with five individuals and sessions lasting one minute for each class of mental state in order to train and test different methods. Given the proposed set of features extracted from the EEG headband five signals (alpha, beta, theta, delta, gamma), we have tested a combination of different features selection algorithms and classifier models to compare their performance in terms of recognition accuracy and number of features needed. Different tests such as 10-fold cross validation were performed. Results show that only 44 features from a set of over 2100 features are necessary when used with classical classifiers such as Bayesian Networks, Support Vector Machines and Random Forests, attaining an overall accuracy over 87%.