EEG Events Patterns Recognition for Robotics Reasoning and Decision Enhancement

Electroencephalography (EEG) based robotics systems, are receiving substantial attentions due to the mass amount of information hidden with human brainwaves. Classification and recognition of electroencephalography of human brainwaves for eye thoughts and movements, is presented within this article. The adopted technique is based on the use of Random Forests (RF) learning paradigm, to classify dissimilar events features, and different human thoughts while looking at images and creating thoughts about the observed images. Features extraction is an essentials stage within this hierarchy, therefore two classes of features extractions were adopted and combined, this include the time domain (TD), and the spectral properties (FD) features. Outcomes of the EEG-related patterns recognition are hence fed into a higher level of decision-making paradigm using fuzzy-based decision system, in such a way to acquire a robotic system diverse robotic tasks and movements. Development of Random Forest classification for the EEG-Robotic system, has shown to be an effective method for modem robotics uses and applications.