An Automatic Driver Assistant Based on Intention Detecting using EEG Signal

Reza Amini Gougeh; Tohid Yousefi Rezaii; Ali Farzamnia

doi:10.1007/978-981-15-5281-6_43

An Automatic Driver Assistant Based on Intention Detecting using EEG Signal

Reza Amini Gougeh, Tohid Yousefi Rezaii, Ali Farzamnia

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

5 Citations (Scopus)

Abstract

Each year, vehicle safety is increasing. Recently brain signals were used to assist drivers. Attempting to do movement produces electrical signals in specific regions of the brain. We developed a system based on motor intention to assist drivers and prevent car accidents. The main objective of this work is improving reaction time to external hazards. The motor intention was recorded by 16 channels of a portable device called Open-BCI. Extracting features was done by common spatial patterns which is a well-known method in motor imagery based brain computer interface (BCI) systems. By using enhanced common spatial pattern (CSP) called strong uncorrelated transform complex common spatial pattern (SUTCCSP), features of preprocessed data were extracted. Regarding the nonlinear nature of electroencephalogram (EEG), support vector machine (SVM) with kernel trick classifier was used to classify features into 3 classes: left, right and brake. Due to using developed SVM, commands can be predicted 500 ms earlier with the system accuracy of 94.6% on average.

Original language	English
Title of host publication	Proceedings of the 11th National Technical Seminar on Unmanned System Technology 2019
Subtitle of host publication	NUSYS 2019
Editors	Zainah Md Zain, Hamzah Ahmad, Dwi Pebrianti, Mahfuzah Mustafa, Nor Rul Hasma Abdullah, Rosdiyana Samad, Maziyah Mat Noh
Publisher	Springer Singapore
Pages	617-627
Number of pages	11
Volume	666
Edition	1st
ISBN (Electronic)	9789811552816
ISBN (Print)	9789811552809, 9789811552830
DOIs	https://doi.org/10.1007/978-981-15-5281-6_43
Publication status	Published - 8 Jul 2020
Externally published	Yes
Event	11th National Technical Symposium on Unmanned System Technology - Kuantan, Malaysia Duration: 2 Dec 2019 → 3 Dec 2019 Conference number: 11

Publication series

Name	Lecture Notes in Electrical Engineering
Publisher	Springer
Volume	666
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	11th National Technical Symposium on Unmanned System Technology
Abbreviated title	NUSYS 2019
Country/Territory	Malaysia
City	Kuantan
Period	2/12/19 → 3/12/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-981-15-5281-6_43Licence: Unspecified

Cite this

Gougeh, R. A., Rezaii, T. Y., & Farzamnia, A. (2020). An Automatic Driver Assistant Based on Intention Detecting using EEG Signal. In Z. Md Zain, H. Ahmad, D. Pebrianti, M. Mustafa, N. R. H. Abdullah, R. Samad, & M. Mat Noh (Eds.), Proceedings of the 11th National Technical Seminar on Unmanned System Technology 2019: NUSYS 2019 (1st ed., Vol. 666, pp. 617-627). (Lecture Notes in Electrical Engineering; Vol. 666). Springer Singapore. https://doi.org/10.1007/978-981-15-5281-6_43

Gougeh, Reza Amini ; Rezaii, Tohid Yousefi ; Farzamnia, Ali. / An Automatic Driver Assistant Based on Intention Detecting using EEG Signal. Proceedings of the 11th National Technical Seminar on Unmanned System Technology 2019: NUSYS 2019. editor / Zainah Md Zain ; Hamzah Ahmad ; Dwi Pebrianti ; Mahfuzah Mustafa ; Nor Rul Hasma Abdullah ; Rosdiyana Samad ; Maziyah Mat Noh. Vol. 666 1st. ed. Springer Singapore, 2020. pp. 617-627 (Lecture Notes in Electrical Engineering).

@inproceedings{9e6e562e5c1f441eb3190ce0176ad0d2,

title = "An Automatic Driver Assistant Based on Intention Detecting using EEG Signal",

abstract = "Each year, vehicle safety is increasing. Recently brain signals were used to assist drivers. Attempting to do movement produces electrical signals in specific regions of the brain. We developed a system based on motor intention to assist drivers and prevent car accidents. The main objective of this work is improving reaction time to external hazards. The motor intention was recorded by 16 channels of a portable device called Open-BCI. Extracting features was done by common spatial patterns which is a well-known method in motor imagery based brain computer interface (BCI) systems. By using enhanced common spatial pattern (CSP) called strong uncorrelated transform complex common spatial pattern (SUTCCSP), features of preprocessed data were extracted. Regarding the nonlinear nature of electroencephalogram (EEG), support vector machine (SVM) with kernel trick classifier was used to classify features into 3 classes: left, right and brake. Due to using developed SVM, commands can be predicted 500 ms earlier with the system accuracy of 94.6% on average.",

keywords = "BCI, Driving assistant, Intentional EEG",

author = "Gougeh, {Reza Amini} and Rezaii, {Tohid Yousefi} and Ali Farzamnia",

note = "Funding Information: The authors appreciate those who contributed to make this research successful. This research is supported by Center for Research and Innovation (PPPI) and Faculty of Engineering, Universiti Malaysia Sabah (UMS) under the Research Grant (SBK0393-2018). Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd 2021.; 11th National Technical Symposium on Unmanned System Technology, NUSYS 2019 ; Conference date: 02-12-2019 Through 03-12-2019",

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language = "English",

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pages = "617--627",

editor = "{Md Zain}, Zainah and Hamzah Ahmad and Dwi Pebrianti and Mahfuzah Mustafa and Abdullah, {Nor Rul Hasma} and Rosdiyana Samad and {Mat Noh}, Maziyah",

booktitle = "Proceedings of the 11th National Technical Seminar on Unmanned System Technology 2019",

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Gougeh, RA, Rezaii, TY & Farzamnia, A 2020, An Automatic Driver Assistant Based on Intention Detecting using EEG Signal. in Z Md Zain, H Ahmad, D Pebrianti, M Mustafa, NRH Abdullah, R Samad & M Mat Noh (eds), Proceedings of the 11th National Technical Seminar on Unmanned System Technology 2019: NUSYS 2019. 1st edn, vol. 666, Lecture Notes in Electrical Engineering, vol. 666, Springer Singapore, pp. 617-627, 11th National Technical Symposium on Unmanned System Technology, Kuantan, Malaysia, 2/12/19. https://doi.org/10.1007/978-981-15-5281-6_43

An Automatic Driver Assistant Based on Intention Detecting using EEG Signal. / Gougeh, Reza Amini; Rezaii, Tohid Yousefi; Farzamnia, Ali.

Proceedings of the 11th National Technical Seminar on Unmanned System Technology 2019: NUSYS 2019. ed. / Zainah Md Zain; Hamzah Ahmad; Dwi Pebrianti; Mahfuzah Mustafa; Nor Rul Hasma Abdullah; Rosdiyana Samad; Maziyah Mat Noh. Vol. 666 1st. ed. Springer Singapore, 2020. p. 617-627 (Lecture Notes in Electrical Engineering; Vol. 666).

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

TY - GEN

T1 - An Automatic Driver Assistant Based on Intention Detecting using EEG Signal

AU - Gougeh, Reza Amini

AU - Rezaii, Tohid Yousefi

AU - Farzamnia, Ali

N1 - Conference code: 11

PY - 2020/7/8

Y1 - 2020/7/8

N2 - Each year, vehicle safety is increasing. Recently brain signals were used to assist drivers. Attempting to do movement produces electrical signals in specific regions of the brain. We developed a system based on motor intention to assist drivers and prevent car accidents. The main objective of this work is improving reaction time to external hazards. The motor intention was recorded by 16 channels of a portable device called Open-BCI. Extracting features was done by common spatial patterns which is a well-known method in motor imagery based brain computer interface (BCI) systems. By using enhanced common spatial pattern (CSP) called strong uncorrelated transform complex common spatial pattern (SUTCCSP), features of preprocessed data were extracted. Regarding the nonlinear nature of electroencephalogram (EEG), support vector machine (SVM) with kernel trick classifier was used to classify features into 3 classes: left, right and brake. Due to using developed SVM, commands can be predicted 500 ms earlier with the system accuracy of 94.6% on average.

AB - Each year, vehicle safety is increasing. Recently brain signals were used to assist drivers. Attempting to do movement produces electrical signals in specific regions of the brain. We developed a system based on motor intention to assist drivers and prevent car accidents. The main objective of this work is improving reaction time to external hazards. The motor intention was recorded by 16 channels of a portable device called Open-BCI. Extracting features was done by common spatial patterns which is a well-known method in motor imagery based brain computer interface (BCI) systems. By using enhanced common spatial pattern (CSP) called strong uncorrelated transform complex common spatial pattern (SUTCCSP), features of preprocessed data were extracted. Regarding the nonlinear nature of electroencephalogram (EEG), support vector machine (SVM) with kernel trick classifier was used to classify features into 3 classes: left, right and brake. Due to using developed SVM, commands can be predicted 500 ms earlier with the system accuracy of 94.6% on average.

KW - BCI

KW - Driving assistant

KW - Intentional EEG

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M3 - Conference contribution

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SN - 9789811552830

VL - 666

T3 - Lecture Notes in Electrical Engineering

SP - 617

EP - 627

BT - Proceedings of the 11th National Technical Seminar on Unmanned System Technology 2019

A2 - Md Zain, Zainah

A2 - Ahmad, Hamzah

A2 - Pebrianti, Dwi

A2 - Mustafa, Mahfuzah

A2 - Abdullah, Nor Rul Hasma

A2 - Samad, Rosdiyana

A2 - Mat Noh, Maziyah

PB - Springer Singapore

T2 - 11th National Technical Symposium on Unmanned System Technology

Y2 - 2 December 2019 through 3 December 2019

ER -

Gougeh RA, Rezaii TY, Farzamnia A. An Automatic Driver Assistant Based on Intention Detecting using EEG Signal. In Md Zain Z, Ahmad H, Pebrianti D, Mustafa M, Abdullah NRH, Samad R, Mat Noh M, editors, Proceedings of the 11th National Technical Seminar on Unmanned System Technology 2019: NUSYS 2019. 1st ed. Vol. 666. Springer Singapore. 2020. p. 617-627. (Lecture Notes in Electrical Engineering). Epub 2020 Jul 7. doi: 10.1007/978-981-15-5281-6_43

An Automatic Driver Assistant Based on Intention Detecting using EEG Signal

Abstract

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Conference

UN SDGs

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