EEG-based Universal Prediction Model of Emergency Braking Intention for Brain-controlled Vehicles

Xiaoguang Wang; Luzheng Bi; Weijie Fei; Jinling Lian; Huikang Wang

doi:10.1109/NER.2019.8717049

EEG-based Universal Prediction Model of Emergency Braking Intention for Brain-controlled Vehicles

Xiaoguang Wang, Luzheng Bi^*, Weijie Fei, Jinling Lian, Huikang Wang

^*Corresponding author for this work

School of Mechanical Engineering

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Citations (Scopus)

Abstract

Electroencephalogram (EEG)-based prediction of driver emergency braking intention can help develop an assistance system to improve driving safety for brain-controlled vehicles. However, existing studies are focused on how to build an individual detection model for each participant. In this paper, to build a universal model, a convolutional neural network (CNN) is used to extract the features of brain signals and build the universal model. Experimental results from 13 subjects show that the proposed CNN-based method outperforms the linear discriminant analysis (LDA)-based method and has a comparable performance with individual models. This work lays a foundation for future developments of an EEG-based universal model of driver emergency braking intention detection.

Original language	English
Title of host publication	9th International IEEE EMBS Conference on Neural Engineering, NER 2019
Publisher	IEEE Computer Society
Pages	389-392
Number of pages	4
ISBN (Electronic)	9781538679210
DOIs	https://doi.org/10.1109/NER.2019.8717049
Publication status	Published - 16 May 2019
Event	9th International IEEE EMBS Conference on Neural Engineering, NER 2019 - San Francisco, United States Duration: 20 Mar 2019 → 23 Mar 2019

Publication series

Name	International IEEE/EMBS Conference on Neural Engineering, NER
Volume	2019-March
ISSN (Print)	1948-3546
ISSN (Electronic)	1948-3554

Conference

Conference	9th International IEEE EMBS Conference on Neural Engineering, NER 2019
Country/Territory	United States
City	San Francisco
Period	20/03/19 → 23/03/19

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10.1109/NER.2019.8717049

Cite this

Wang, X., Bi, L., Fei, W., Lian, J., & Wang, H. (2019). EEG-based Universal Prediction Model of Emergency Braking Intention for Brain-controlled Vehicles. In 9th International IEEE EMBS Conference on Neural Engineering, NER 2019 (pp. 389-392). Article 8717049 (International IEEE/EMBS Conference on Neural Engineering, NER; Vol. 2019-March). IEEE Computer Society. https://doi.org/10.1109/NER.2019.8717049

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title = "EEG-based Universal Prediction Model of Emergency Braking Intention for Brain-controlled Vehicles",

abstract = "Electroencephalogram (EEG)-based prediction of driver emergency braking intention can help develop an assistance system to improve driving safety for brain-controlled vehicles. However, existing studies are focused on how to build an individual detection model for each participant. In this paper, to build a universal model, a convolutional neural network (CNN) is used to extract the features of brain signals and build the universal model. Experimental results from 13 subjects show that the proposed CNN-based method outperforms the linear discriminant analysis (LDA)-based method and has a comparable performance with individual models. This work lays a foundation for future developments of an EEG-based universal model of driver emergency braking intention detection.",

author = "Xiaoguang Wang and Luzheng Bi and Weijie Fei and Jinling Lian and Huikang Wang",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 9th International IEEE EMBS Conference on Neural Engineering, NER 2019 ; Conference date: 20-03-2019 Through 23-03-2019",

year = "2019",

month = may,

day = "16",

doi = "10.1109/NER.2019.8717049",

language = "English",

series = "International IEEE/EMBS Conference on Neural Engineering, NER",

publisher = "IEEE Computer Society",

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}

Wang, X, Bi, L, Fei, W, Lian, J & Wang, H 2019, EEG-based Universal Prediction Model of Emergency Braking Intention for Brain-controlled Vehicles. in 9th International IEEE EMBS Conference on Neural Engineering, NER 2019., 8717049, International IEEE/EMBS Conference on Neural Engineering, NER, vol. 2019-March, IEEE Computer Society, pp. 389-392, 9th International IEEE EMBS Conference on Neural Engineering, NER 2019, San Francisco, United States, 20/03/19. https://doi.org/10.1109/NER.2019.8717049

EEG-based Universal Prediction Model of Emergency Braking Intention for Brain-controlled Vehicles. / Wang, Xiaoguang; Bi, Luzheng; Fei, Weijie et al.
9th International IEEE EMBS Conference on Neural Engineering, NER 2019. IEEE Computer Society, 2019. p. 389-392 8717049 (International IEEE/EMBS Conference on Neural Engineering, NER; Vol. 2019-March).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Wang, Huikang

PY - 2019/5/16

Y1 - 2019/5/16

N2 - Electroencephalogram (EEG)-based prediction of driver emergency braking intention can help develop an assistance system to improve driving safety for brain-controlled vehicles. However, existing studies are focused on how to build an individual detection model for each participant. In this paper, to build a universal model, a convolutional neural network (CNN) is used to extract the features of brain signals and build the universal model. Experimental results from 13 subjects show that the proposed CNN-based method outperforms the linear discriminant analysis (LDA)-based method and has a comparable performance with individual models. This work lays a foundation for future developments of an EEG-based universal model of driver emergency braking intention detection.

AB - Electroencephalogram (EEG)-based prediction of driver emergency braking intention can help develop an assistance system to improve driving safety for brain-controlled vehicles. However, existing studies are focused on how to build an individual detection model for each participant. In this paper, to build a universal model, a convolutional neural network (CNN) is used to extract the features of brain signals and build the universal model. Experimental results from 13 subjects show that the proposed CNN-based method outperforms the linear discriminant analysis (LDA)-based method and has a comparable performance with individual models. This work lays a foundation for future developments of an EEG-based universal model of driver emergency braking intention detection.

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M3 - Conference contribution

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BT - 9th International IEEE EMBS Conference on Neural Engineering, NER 2019

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EEG-based Universal Prediction Model of Emergency Braking Intention for Brain-controlled Vehicles

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