Emergency Braking Intention Detect System Based on K-Order Propagation Number Algorithm: A Network Perspective

In order to avoid erroneous braking responses when vehicle drivers are faced with a stressful setting, a <i>K-order propagation number algorithm–Feature selection–Classification System (KFCS)</i> is developed in this paper to detect emergency braking intentions in simulated driving scena...

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Autores principales:	Yuhong Zhang, Yuan Liao, Yudi Zhang, Liya Huang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	brain-computer interface technology (BCI) electroencephalogram (EEG) braking intention detect brain network K-order structure entropy pattern recognition Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/0351a45926cf408188de7979cb65dae1
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id	oai:doaj.org-article:0351a45926cf408188de7979cb65dae1
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spelling	oai:doaj.org-article:0351a45926cf408188de7979cb65dae12021-11-25T16:56:49ZEmergency Braking Intention Detect System Based on K-Order Propagation Number Algorithm: A Network Perspective10.3390/brainsci111114242076-3425https://doaj.org/article/0351a45926cf408188de7979cb65dae12021-10-01T00:00:00Zhttps://www.mdpi.com/2076-3425/11/11/1424https://doaj.org/toc/2076-3425In order to avoid erroneous braking responses when vehicle drivers are faced with a stressful setting, a <i>K-order propagation number algorithm–Feature selection–Classification System (KFCS)</i> is developed in this paper to detect emergency braking intentions in simulated driving scenarios using electroencephalography (EEG) signals. Two approaches are employed in KFCS to extract EEG features and to improve classification performance: the K-Order Propagation Number Algorithm is the former, calculating the node importance from the perspective of brain networks as a novel approach; the latter uses a set of feature extraction algorithms to adjust the thresholds. Working with the data collected from seven subjects, the highest classification accuracy of a single trial can reach over 90%, with an overall accuracy of 83%. Furthermore, this paper attempts to investigate the mechanisms of brain activeness under two scenarios by using a topography technique at the sensor-data level. The results suggest that the active regions at two states is different, which leaves further exploration for future investigations.Yuhong ZhangYuan LiaoYudi ZhangLiya HuangMDPI AGarticlebrain-computer interface technology (BCI)electroencephalogram (EEG)braking intention detectbrain networkK-order structure entropypattern recognitionNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENBrain Sciences, Vol 11, Iss 1424, p 1424 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	brain-computer interface technology (BCI) electroencephalogram (EEG) braking intention detect brain network K-order structure entropy pattern recognition Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	brain-computer interface technology (BCI) electroencephalogram (EEG) braking intention detect brain network K-order structure entropy pattern recognition Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Yuhong Zhang Yuan Liao Yudi Zhang Liya Huang Emergency Braking Intention Detect System Based on K-Order Propagation Number Algorithm: A Network Perspective
description	In order to avoid erroneous braking responses when vehicle drivers are faced with a stressful setting, a <i>K-order propagation number algorithm–Feature selection–Classification System (KFCS)</i> is developed in this paper to detect emergency braking intentions in simulated driving scenarios using electroencephalography (EEG) signals. Two approaches are employed in KFCS to extract EEG features and to improve classification performance: the K-Order Propagation Number Algorithm is the former, calculating the node importance from the perspective of brain networks as a novel approach; the latter uses a set of feature extraction algorithms to adjust the thresholds. Working with the data collected from seven subjects, the highest classification accuracy of a single trial can reach over 90%, with an overall accuracy of 83%. Furthermore, this paper attempts to investigate the mechanisms of brain activeness under two scenarios by using a topography technique at the sensor-data level. The results suggest that the active regions at two states is different, which leaves further exploration for future investigations.
format	article
author	Yuhong Zhang Yuan Liao Yudi Zhang Liya Huang
author_facet	Yuhong Zhang Yuan Liao Yudi Zhang Liya Huang
author_sort	Yuhong Zhang
title	Emergency Braking Intention Detect System Based on K-Order Propagation Number Algorithm: A Network Perspective
title_short	Emergency Braking Intention Detect System Based on K-Order Propagation Number Algorithm: A Network Perspective
title_full	Emergency Braking Intention Detect System Based on K-Order Propagation Number Algorithm: A Network Perspective
title_fullStr	Emergency Braking Intention Detect System Based on K-Order Propagation Number Algorithm: A Network Perspective
title_full_unstemmed	Emergency Braking Intention Detect System Based on K-Order Propagation Number Algorithm: A Network Perspective
title_sort	emergency braking intention detect system based on k-order propagation number algorithm: a network perspective
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/0351a45926cf408188de7979cb65dae1
work_keys_str_mv	AT yuhongzhang emergencybrakingintentiondetectsystembasedonkorderpropagationnumberalgorithmanetworkperspective AT yuanliao emergencybrakingintentiondetectsystembasedonkorderpropagationnumberalgorithmanetworkperspective AT yudizhang emergencybrakingintentiondetectsystembasedonkorderpropagationnumberalgorithmanetworkperspective AT liyahuang emergencybrakingintentiondetectsystembasedonkorderpropagationnumberalgorithmanetworkperspective
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Emergency Braking Intention Detect System Based on K-Order Propagation Number Algorithm: A Network Perspective

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