Electroencephalography Features Extraction and Deep Patterns Analysis for Robotics Learning and Control through Brain-Computer Interface

Abstract

Electroencephalography (EEG) has been a recent subject for various medical, clinical and non-clinical uses and applications. This is due to the extensive amount of information hidden within the human EEG brainwaves and brain neural activities. Within this article, we shall present another novel approach related to mining with well, healthy, and clinically approved EEG brainwaves related to events related to human vision EEG, and how a robotics system can be controlled and moved through such Brain-Computer Interface (BCI). The adopted thought is more related to the usage of time related features, and the time-spectral (wavelet) features extraction, hence to recognize the complicated eye-thoughts EEG patterns through a recognition algorithm. Two approaches have been used for the pattern recognition, this include the SVM, and the PAC based Random Forest with 30 trees. Results also include how a robotics system can be controlled using the EEG brainwaves.