Noninvasive Brain-Computer Interface-based Control of Humanoid Navigation

This study proposes an asynchronous noninvasive Brain Computer Interface (BCI) -based navigation system for a humanoid robot, which can behave similarly to a human. In the experimental procedure, each subject is asked to undertake three different sessions: offline training, an online feedback test, and real-time control of a humanoid robot in an indoor maze. During the offline training session, amplitude features from the EEG are extracted using auto-regressive frequency analysis with a Laplacian filter. The optimal feature components are selected by using the Fisher ratio and the linear discriminant analysis (LDA) distance metric. Two classifiers are hierarchically set to build the asynchronous BCI system. During the online test session, the trained BCI system translates a subject's ongoing EEG into four mental states: rest, left-hand imagery, right-hand imagery, and foot imagery. Event-by-event analysis is applied to evaluate the performance of the BCI system. If the test performance is consistently satisfactory, the subject executes the real-time control experiments. During the navigation experiments, the subject controls the robot in an indoor maze using the BCI system while surveying the environment through visual feedback. The results show that BCI control was comparable to manual control with a performance ratio of 81%. The evaluation of the results validates the feasibility and power of the proposed system.