P300 BCI based planning behavior selection network for humanoid robot control

We propose planning behavior selection network (PBSN) based brain-computer interface (BCI) for controlling a humanoid robot. BCI provides commands to external devices or computer applications only using user´s brain signals. However, few commands from BCI cause user fatigue. PBSN is a hybrid method between reactive system and goal-oriented planning system. PBSN has two beneficial points. One is robustness of reactive system and the other is long-term goal planning of planning system. This only requires high-level commands from the user and frees from make low level command to operate the robot. Finally, it makes possible to reduce user´s fatigue. Online accuracy test gives reasonable accuracy rate, and PBSN based online simulation shows possibility as an assistant humanoid robot.