@article{(Open Science Index):https://publications.waset.org/pdf/10013100,
	  title     = {Electroencephalography-Based Intention Recognition and Consensus Assessment during Emergency Response},
	  author    = {Siyao Zhu and  Yifang Xu},
	  country	= {},
	  institution	= {},
	  abstract     = {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.},
	    journal   = {International Journal of Computer and Systems Engineering},
	  volume    = {17},
	  number    = {5},
	  year      = {2023},
	  pages     = {318 - 327},
	  ee        = {https://publications.waset.org/pdf/10013100},
	  url   	= {https://publications.waset.org/vol/197},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 197, 2023},
	}