Control of bipedal locomotion with a neural oscillator-based brain-computer interface

This study proposes a neural oscillator-based brain-computer interface (BCI) that controls a bipedal neuromusculoskeletal (NMS) model by inputting electroencephalogram (EEG) signals. In this BCI system, while the bipedal NMS system realizes bipedal locomotion through internal entrainment among neural oscillators and a musculoskeletal system, the locomotion of the system is controlled via external entrainment of the neural oscillators to the external input of EEG signals. As the first step in developing the neural oscillator-based BCI controlling a bipedal NMS model, exploratory numerical simulations were conducted to investigate the behavior of the proposed BCI when sinusoidal waves and alpha waves were inputted. The following tendencies were observed: (a) inputting sinusoidal waves with small amplitudes and high frequencies did not affect the natural walking behavior of the bipedal NMS model that was generated by including only offset values in the external input, (b) inputting sinusoidal waves with small amplitudes and low frequencies disturbed and decelerated the walking behavior, (c) inputting sinusoidal waves with large amplitudes accelerated the walking behavior, (d) inputting sinusoidal waves with large amplitudes and a particular frequency changed walking behavior to running behavior, (e) changing the external input of alpha waves between an eyes-open condition and an eyes-closed condition successfully changed the walking behavior. The eyes-open condition led to faster walking compared with the eyes-closed condition.