%0 Journal Article %A Siyao Zhu and Yifang Xu %D 2023 %J International Journal of Computer and Systems Engineering %B World Academy of Science, Engineering and Technology %I Open Science Index 197, 2023 %T Electroencephalography-Based Intention Recognition and Consensus Assessment during Emergency Response %U https://publications.waset.org/pdf/10013100 %V 197 %X After natural and man-made disasters, robots can bypass the danger, expedite the search, and acquire unprecedented situational awareness to design rescue plans. Brain-computer interface is a promising option to overcome the limitations of tedious manual control and operation of robots in the urgent search-and-rescue tasks. This study aims to test the feasibility of using electroencephalography (EEG) signals to decode human intentions and detect the level of consensus on robot-provided information. EEG signals were classified using machine-learning and deep-learning methods to discriminate search intentions and agreement perceptions. The results show that the average classification accuracy for intention recognition and consensus assessment is 67% and 72%, respectively, proving the potential of incorporating recognizable users’ bioelectrical responses into advanced robot-assisted systems for emergency response. %P 318 - 327