TY - GEN
T1 - Detecting driver's braking intention using recurrent convolutional neural networks based EEG Analysis
AU - Lee, Suk Min
AU - Kim, Jeong Woo
AU - Lee, Seong Whan
N1 - 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.
PY - 2018/12/13
Y1 - 2018/12/13
N2 - 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.
AB - 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.
KW - Brain-Computer Interface (BCI)
KW - Electroencephalography (EEG)
KW - Emergency Braking
KW - Event-Related Potential (ERP)
KW - Recurrent Convolutional Neural Networks (RCNN)
UR - http://www.scopus.com/inward/record.url?scp=85060549606&partnerID=8YFLogxK
U2 - 10.1109/ACPR.2017.86
DO - 10.1109/ACPR.2017.86
M3 - Conference contribution
AN - SCOPUS:85060549606
T3 - Proceedings - 4th Asian Conference on Pattern Recognition, ACPR 2017
SP - 846
EP - 851
BT - Proceedings - 4th Asian Conference on Pattern Recognition, ACPR 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th Asian Conference on Pattern Recognition, ACPR 2017
Y2 - 26 November 2017 through 29 November 2017
ER -