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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.

applied sciences

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