Robot Motion Control via an EEG-Based Brain–Computer Interface by Using Neural Networks and Alpha Brainwaves

TLDR

Brain–computer interface technologies now enable control of devices, and brain‑controlled mobile robots can assist individuals with neuromuscular disorders to improve mobility and quality of life. This study develops a mobile‑robot motion‑control system that interprets a human operator’s eye blinks via a synchronous endogenous EEG BCI using alpha brain waves. EEG signals are filtered to extract features that are input to a trained neural network which directs the robotic vehicle. Experiments with 12 healthy subjects showed the system could command forward, left, backward, and right movements with 92.1% overall accuracy.

Abstract

Modern achievements accomplished in both cognitive neuroscience and human–machine interaction technologies have enhanced the ability to control devices with the human brain by using Brain–Computer Interface systems. Particularly, the development of brain-controlled mobile robots is very important because systems of this kind can assist people, suffering from devastating neuromuscular disorders, move and thus improve their quality of life. The research work presented in this paper, concerns the development of a system which performs motion control in a mobile robot in accordance to the eyes’ blinking of a human operator via a synchronous and endogenous Electroencephalography-based Brain–Computer Interface, which uses alpha brain waveforms. The received signals are filtered in order to extract suitable features. These features are fed as inputs to a neural network, which is properly trained in order to properly guide the robotic vehicle. Experimental tests executed on 12 healthy subjects of various gender and age, proved that the system developed is able to perform movements of the robotic vehicle, under control, in forward, left, backward, and right direction according to the alpha brainwaves of its operator, with an overall accuracy equal to 92.1%.