A Hardware in Loop Simulation Robot Control by Weareable Electroencephalography (EEG)-Based Brain Computer Interface (BCI)

Brain Computer Interfaces (BCI) translating brain wave signals into practical commands to operate external devices by which augment human capabilities. However, many issues face the development of BCIs such as how to extract commands from EEGs due to the low signal-to-noise ratio (SNR) of EEG signals. This paper investigates an EEG-driven hardware-in-loop (HIL) experimental robot for BCI stimulation system individualized design and validation. Based on power spectrum data collected in real-time by the two TGAM electrodes, we developed a novel BCI stimulation system that allows us to adjust robot navigation. By using the SVM model, the EEG signals are preprocessed and converted into mental commands (e.g. forward, left …) to navigate the simulated robot. The average accuracy of the robot movement was 62.6%, which obtained Cohen's Kappa coefficient are significantly better than chance (κ = 0.50). Our results showed that the robot control can be achieved with reduced accuracy under the respective experimental conditions in a simulation environment.