An EEG-based brain-computer interface for dual task driving detection

Authors:
Chin-Teng Lin;Yu-Kai Wang;Shi-An Chen
Affiliations:
Brain Research Center, Department of Computer Science, National Chiao-Tung University, Hsinchu, Taiwan;Brain Research Center, Department of Computer Science, National Chiao-Tung University, Hsinchu, Taiwan;Brain Research Center, Department of Computer Science, National Chiao-Tung University, Hsinchu, Taiwan
Venue:
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
Year:
2011

Citing 6
Cited 1

Independent component analysis, a new concept?

Signal Processing - Special issue on higher order statistics
A family of fuzzy learning algorithms for robust principal component analysis neural networks

IEEE Transactions on Fuzzy Systems
Implementation of fuzzy cognitive maps based on fuzzy neural network and application in prediction of time series

IEEE Transactions on Fuzzy Systems
A locally recurrent fuzzy neural network with support vector regression for dynamic-system modeling

IEEE Transactions on Fuzzy Systems
An interval type-2 fuzzy-neural network with support-vector regression for noisy regression problems

IEEE Transactions on Fuzzy Systems
A self-organizing fuzzy neural network based on a growing-and-pruning algorithm

IEEE Transactions on Fuzzy Systems

An EEG-based brain-computer interface for dual task driving detection

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

A novel detective model for driver distraction was proposed in this study. Driver distraction is a significant cause of traffic accidents during these years. To study human cognition under a specific driving task, one virtual reality (VR)-based simulation was built. Unexpected car deviations and mathematics questions with stimulus onset asynchrony (SOA) were designed. Electroencephalography (EEG) is a good index for the distraction level to monitor the effects of the dual tasks. Power changing in Frontal and Motor cortex were extracted for the detective model by independent component analysis (ICA). All distracting and non-distracting EEG epochs could be revealed the existence by self-organizing map (SOM). The results presented that this system approached about 90% accuracy to recognize the EEG epochs of non-distracting driving, and might be practicable for daily life.