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 EEGrelated patterns recognition are hence fed into a higher level of decisionmaking paradigm using fuzzybased decision system, in such a way to acquire a robotic system diverse robotic tasks and movements. Development of Random Forest classification for the EEGRobotic system, has shown to be an effective method for modem robotics uses and applications.

Identification of epileptic discharge based on statistical analysis and fractal analysis Epilepsy is a relatively common brain disorder characterized by transient but recurrent abnormal discharge of neurons due to the dysfunction of the central nervous system. Brainwave EEG signals are customary used in clinical diagnosis and screening of epileptic seizure patients. EEG abnormalities include abnormal background waves, very short epileptic discharges (lasting only about several tens of milliseconds), and seizure signals (lasting a few seconds). There are 7 classes of short epileptic discharges, identification of which is often considered an effective screening of epileptic seizure patients. In this study, we consider classification of these short epileptic discharges. For this purpose, we analyzed 422 multichannel EEG segments, each 4  s  long. Among these segments, 322 are short epileptic discharges, 100 are from healthy controls. We have first extracted features from these EEG segments using statistical analysis and Adaptive Fractal Analysis (AFA), then used Random Forest Classifier to identify and classify all 7 epileptic discharges. We have achieved very high recognition and classification accuracy with this synthesized approach.