A Nature-Inspired System For Mental State Recognition

In this article, we apply metaheuristics and Neural Networks for classifying human mental activities using EEG signals. We developed a Brain-Computer Interface system that is able to classify mental concentration versus relaxation. We collect the brain information during specific activities of the subject. Besides, we selected the best combination of the input features using the following two metaheuristic techniques: Simulated Annealing and Geometric Particle Swarm Optimization. The classification problem is solved using Neural Networks. We show that is possible to identify the human concentration using few EEG signals. In addition, the proposed system is developed with a fast and robust learning technique that can be easily adapted according to each subject.