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|Title:||A novel method of emergency situation detection for a brain-controlled vehicle by combining EEG signals with surrounding information||Authors:||Bi, Luzheng
|Keywords:||Engineering::Computer science and engineering||Issue Date:||2018||Source:||Bi, L., Wang, H., Teng, T., & Guan, C. (2018). A novel method of emergency situation detection for a brain-controlled vehicle by combining EEG signals with surrounding information. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 26(10), 1926-1934. doi:10.1109/TNSRE.2018.2868486||Journal:||IEEE Transactions on Neural Systems and Rehabilitation Engineering||Abstract:||In this paper, to address the safety of brain-controlled vehicles under emergency situations, we propose a novel method of emergency situation detection by fusing driver electroencephalography (EEG) signals with surrounding information. We first build a novel EEG-based detection model of driver emergency braking intention. We then recognize emergency situations by fusing the result of the proposed EEG-based intention detection model with that of the obstacle detection model based on surrounding information. The real-time detection system of driver emergency braking intention is implemented on an embedded system, and the driver-and-hardware-in-the-loop-experiment of the proposed detection method of emergency situations is performed. Experimental results show that the proposed method can detect emergency situations with the system accuracy of 94.89%, false alarm rate of 0.05%, and response time of 540 ms. This paper has important values in the future development of brain-controlled vehicles, human-centric advanced driver assistant systems, and self-driving vehicles and opens a new avenue on how cognitive neuroscience may be applied to human-machine integration.||URI:||https://hdl.handle.net/10356/139876||ISSN:||1534-4320||DOI:||10.1109/TNSRE.2018.2868486||Rights:||© 2018 IEEE. All rights reserved.||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||SCSE Journal Articles|
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