Classification of mental tasks using Gaussian mixture Bayesian network classifiers

In this work we consider classification of mental tasks from EEG signals by using Gaussian mixture models. For this purpose, we use Bayesian graphical networks (BNT). The final results for Bayesian graphical networks are compared with our previous results for the neural network classifier. The results show an improvement in both classification accuracy and consistency.