Motor Imagery EEGEOG Signals Based Brain Machine Interface (BMI) for a Mobile Robotic Assistant (MRA) Assistive devices for disabled individuals provide the support to fulfil their activities of daily living. The proposed mobile robotic assistant (MRA) in this paper is capable of providing both mobile and manipulation support for users. The MRA, which consists of an electric wheelchair and a custom developed 5DOF robotic manipulator is controlled by a Motor Imagery (MI) Electroencephalography (EEG) and Electrooculography (EOG) based Brain Machine Interface (BMI) which is proposed in this paper. A custom developed Graphical User Interface (GUI) is utilized to interact with the users and users are able to control either the wheelchair or the robotic manipulator based on a combination of left and right hand MIEEG signals and EOG signals via this GUI. A Multilayer Perceptron (MLP) Neural Network based classifier is developed to classify the EEG signals of left vs right vs rest. EOG signals (eyeblinks) are used to activate the task on the GUI menu. A set of experiments have been carried out with healthy subjects and the results show the effectiveness of the proposed methods.

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.