Combined EEG-gyroscope-TDCS brain machine interface system for early management of driver drowsiness

Li, G; Chung, WY

Combined EEG-gyroscope-TDCS brain machine interface system for early management of driver drowsiness

Li, G

Chung, WY

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Human-Machine Systems, 2018, 48 (1), pp. 50 - 62
Issue Date:: 2018-02-01

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Field	Value	Language
dc.contributor.author	Li, G https://orcid.org/0000-0002-5251-7445	en_US
dc.contributor.author	Chung, WY	en_US
dc.date.issued	2018-02-01	en_US
dc.identifier.citation	IEEE Transactions on Human-Machine Systems, 2018, 48 (1), pp. 50 - 62	en_US
dc.identifier.issn	2168-2291	en_US
dc.identifier.uri	http://hdl.handle.net/10453/133079
dc.description.abstract	© 2013 IEEE. In this paper, we present the design and implementation of a wireless, wearable brain machine interface (BMI) system dedicated to signal sensing and processing for driver drowsiness detection (DDD). Owing to the importance of driver drowsiness and the possibility for brainwaves-based DDD, many electroencephalogram (EEG)-based approaches have been proposed. However, few studies focus on the early detection of driver drowsiness and on the early management of driver drowsiness using a closed-loop algorithm. The reported wireless and wearable BMI system is used for 1) simultaneous EEG and gyroscope-based head movement measurement for the early detection of driver drowsiness and 2) simultaneous EEG and transcranial direct current stimulation (tDCS) for the early management of driver drowsiness. To achieve the purposes of easy-To-use and distraction-free driving, a Bluetooth low-energy module is embedded in this BMI system and used to communicate with a fully wearable consumer device, a smartwatch, which coordinates the work of drowsiness monitoring and brain stimulation with its embedded closed-loop algorithm. The proposed system offers a 128 Hz sampling rate per channel, 12-bit and 16-bit resolution for a single-channel EEG and a three-channel gyroscope, and a maximum 2 mA current for the tDCS. The current consumption of the whole headset system is 56 mA. The battery life of the smartwatch is 9 h. The DDD experimental results show that the proposed system obtained a 93.67% five-level overall accuracy, a 96.15% two-level (alert versus slightly drowsy) accuracy, and maximum 16-To 23-min wakefulness maintenance.	en_US
dc.relation.ispartof	IEEE Transactions on Human-Machine Systems	en_US
dc.relation.isbasedon	10.1109/THMS.2017.2759808	en_US
dc.title	Combined EEG-gyroscope-TDCS brain machine interface system for early management of driver drowsiness	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	48	en_US
utslib.for	0903 Biomedical Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	48	en_US

Abstract:

© 2013 IEEE. In this paper, we present the design and implementation of a wireless, wearable brain machine interface (BMI) system dedicated to signal sensing and processing for driver drowsiness detection (DDD). Owing to the importance of driver drowsiness and the possibility for brainwaves-based DDD, many electroencephalogram (EEG)-based approaches have been proposed. However, few studies focus on the early detection of driver drowsiness and on the early management of driver drowsiness using a closed-loop algorithm. The reported wireless and wearable BMI system is used for 1) simultaneous EEG and gyroscope-based head movement measurement for the early detection of driver drowsiness and 2) simultaneous EEG and transcranial direct current stimulation (tDCS) for the early management of driver drowsiness. To achieve the purposes of easy-To-use and distraction-free driving, a Bluetooth low-energy module is embedded in this BMI system and used to communicate with a fully wearable consumer device, a smartwatch, which coordinates the work of drowsiness monitoring and brain stimulation with its embedded closed-loop algorithm. The proposed system offers a 128 Hz sampling rate per channel, 12-bit and 16-bit resolution for a single-channel EEG and a three-channel gyroscope, and a maximum 2 mA current for the tDCS. The current consumption of the whole headset system is 56 mA. The battery life of the smartwatch is 9 h. The DDD experimental results show that the proposed system obtained a 93.67% five-level overall accuracy, a 96.15% two-level (alert versus slightly drowsy) accuracy, and maximum 16-To 23-min wakefulness maintenance.

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http://hdl.handle.net/10453/133079