Continuous Shared Control of a Mobile Robot with Brain–Computer Interface and Autonomous Navigation for Daily Assistance

Although the electroencephalography (EEG) based brain–computer interface (BCI) has been successfully developed for rehabilitation and assistance, it is still challenging to achieve continuous control of a brain-actuated mobile robot system. In this study, we propose shared strategy combining BCI autonomous navigation The weight designed dynamically adjust fusion navigation. During process, system uses visual-based simultaneous localization mapping (SLAM) method construct environmental maps. After obtaining global optimal path, utilizes brain-based dynamic window approach (BSC-DWA) evaluate safe reachable trajectories while considering velocity. Eight subjects participated in two-stage training, six these eight online experiments. training results demonstrated that naïve could performance with an average percent valid correct rate approximately 97% total over 80%. experiments showed all complete tasks unknown corridor control. Therefore, our verified feasibility effectiveness proposed BCI, control, navigation, visual SLAM. framework shows great promise BCI-driven tasks, especially brain-driven assistive robots wheelchairs daily applications.