Abstract
Accidents on the road mostly occur because of human error. Understanding and predicting the manner in which the brain functions when driving can help in reduce fatalities. Particularly, with the recent development of auto-driving cars, it is important to ensure that the driver is ready to retake the control of the vehicle at all times in the event of a system failure. This study attempts to create a brain–computer interface (BCI) using signals obtained through functional near-infrared spectroscopy (fNIRS) to evaluate the impact of different external conditions on the driver’s mental state: weather condition, type of road, including manual driving versus auto-pilot. A deep neural network (DNN) and a recurrent neural network (RNN) are employed for their ability of pattern recognition in the processing of fNIRS signals and are compared to other common classification methods. The results of the study demonstrated that both DNN and RNN offer the same performance. Furthermore, brain activity under different weather conditions cannot be classified by any of the proposed methods. Nevertheless, DNN and RNN have proven their effectiveness in the road type classification with 63% accuracy.
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Acknowledgement
This study was partially supported by the MEXT-Supported Program for the Strategic Research Foundation at Private Universities, 2014–2018, Ministry of Education, Culture, Sports, Science and Technology, Japan.
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Huve, G., Takahashi, K., Hashimoto, M. (2018). fNIRS-Based Brain–Computer Interface Using Deep Neural Networks for Classifying the Mental State of Drivers. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11141. Springer, Cham. https://doi.org/10.1007/978-3-030-01424-7_35
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