A Novel Method of Emergency Situation Detection for a Brain-Controlled Vehicle by Combining EEG Signals with Surrounding Information

Luzheng Bi*, Huikang Wang, Teng Teng, Cuntai Guan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

37 Citations (Scopus)

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.

Original languageEnglish
Article number8454493
Pages (from-to)1926-1934
Number of pages9
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume26
Issue number10
DOIs
Publication statusPublished - Oct 2018

Keywords

  • EEG
  • brain-controlled vehicles
  • braking intention
  • emergency situation

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