Detecting driver's braking intention using recurrent convolutional neural networks based EEG Analysis

Suk Min Lee, Jeong Woo Kim, Seong Whan Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)


Driving assistance system has been recently studied to prevent emergency braking situations by combining external information on radar or camera devices and internal information on driver's intention. Electroencephalography (EEG) is an effective method to read user's intention with high temporal resolution. Our proposed system is mainly contributed to detecting driver's braking intention prior to stepping on the brake pedal in the emergency situation. We investigated early event-related potential (ERP) curves evoked by visual sensory process in emergency situation by using recurrent convolutional neural networks (RCNN) model. RCNN model has advantages to capture contextual and spatial patterns of brain signal. RCNN model is composed of a convolutional layer, two recurrent convolutional layers (RCLs), and a softmax layer. Fourteen participants drove for 120 minutes with two types of emergency situations and a normal driving situation in a virtual driving environment. In this article, early ERP showed a potential to be used for classifying the driver's braking intention. The classification performances based on RCNN and regularized linear discriminant analysis (RLDA) at 200 ms post-stimulus time were 0.86 AUC score and 0.61 AUC score respectively. Following the results, braking intention was recognized at 380 ms earlier based on early ERP patterns using RCNN model than the brake pedal. Our system could be applied to other brain-computer interface (BCI) system for minimizing detection time by capturing early ERP curves based on RCNN model.

Original languageEnglish
Title of host publicationProceedings - 4th Asian Conference on Pattern Recognition, ACPR 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781538633540
Publication statusPublished - 2018 Dec 13
Event4th Asian Conference on Pattern Recognition, ACPR 2017 - Nanjing, China
Duration: 2017 Nov 262017 Nov 29

Publication series

NameProceedings - 4th Asian Conference on Pattern Recognition, ACPR 2017


Other4th Asian Conference on Pattern Recognition, ACPR 2017

Bibliographical note

Funding Information:
ACKNOWLEDGMENTS This work was supported by Institute for Information & Communications TechnologyPromotion (IITP) grant funded by the Korea government (No. 2017-0-00451, Development of BCI based Brain and Cognitive Computing Technologyfor Recognizing Users Intentions using Deep Learning).

Publisher Copyright:
© 2017 IEEE.


  • Brain-Computer Interface (BCI)
  • Electroencephalography (EEG)
  • Emergency Braking
  • Event-Related Potential (ERP)
  • Recurrent Convolutional Neural Networks (RCNN)

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Signal Processing


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