The Application of Integration of EEG Signals for Authorial Classification Algorithms in Implementation for a Mobile Robot Control Using Movement Imagery—Pilot Study

Abstract

This paper presents a new approach to the issue of recognition and classification of electroencephalographic signals (EEG). A small number of investigations using the Emotiv Epoc Flex sensor set was the reason for searching for original solutions including control of elements of robotics with mental orders given by a user. The signal, measured and archived with a 32-electrode device, was prepared for classification using a new solution consisting of EEG signal integration. The new waveforms modified in this way could be subjected to recognition both by a classic authorial software and an artificial neural network. The properly classified signals made it possible to use them as the signals controlling the LEGO EV3 Mindstorms robot.