Real-Time EEG Classification of Voluntary Hand Movement Directions using Brain Machine Interface

Intelligent systems for bio-signals processing and modelling are a method for creating signals to measure the brain activities to perform task by an external device. Brain Machine Interface (BMI) is a part of bioengineering that connects brain with machine directly in order to command and control the machines. Recently, bioengineering researchers are employing BMI techniques to explore advance knowledge for discovering biological fundamental problems. In this paper, we have proposed an ensemble method to improve the prediction accuracy of real time electroencephalogram signals classification and developed a system that can distinguish different human thoughts. Initially, we have collected the brain signals, and then extracted and selected informative features from these signals to engender training and testing data. We have built several classifiers using Artificial Neural Network (ANN), Decision Tree (DT), naive Bayes (NB) classifier, Bagging, Random Forest and compare the performance of these existing learning methods with proposed ensemble classifier. The proposed method achieved 99% and 79% accuracy on average for binary-class and ternary-class classification in compare with existing classifiers. Finally, we have applied the proposed ensemble classifier for developing a brain game that can control the ball using brain signal of voluntary movements without any need of conventional input devices.