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Analyzing Passive BCI Signals to Control Adaptive Automation Devices

1
Information Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh 12371, Saudi Arabia
2
King Abdulaziz City for Science and Technology, National Satellite Technology Center, Riyadh 12354, Saudi Arabia
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2019, 19(14), 3042; https://doi.org/10.3390/s19143042
Received: 26 May 2019 / Revised: 2 July 2019 / Accepted: 4 July 2019 / Published: 10 July 2019
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Abstract

Brain computer interfaces are currently considered to greatly enhance assistive technologies and improve the experiences of people with special needs in the workplace. The proposed adaptive control model for smart offices provides a complete prototype that senses an environment’s temperature and lighting and responds to users’ feelings in terms of their comfort and engagement levels. The model comprises the following components: (a) sensors to sense the environment, including temperature and brightness sensors, and a headset that collects electroencephalogram (EEG) signals, which represent workers’ comfort levels; (b) an application that analyzes workers’ feelings regarding their willingness to adjust to a space based on an analysis of collected data and that determines workers’ attention levels and, thus, engagement; and (c) actuators to adjust the temperature and/or lighting. This research implemented independent component analysis to remove eye movement artifacts from the EEG signals and used an engagement index to calculate engagement levels. This research is expected to add value to research on smart city infrastructures and on assistive technologies to increase productivity in smart offices. View Full-Text
Keywords: Passive Brain Signals; adaptive automation and controller; EOG artifact; independent component analysis; engagement index Passive Brain Signals; adaptive automation and controller; EOG artifact; independent component analysis; engagement index
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Al-Hudhud, G.; Alqahtani, L.; Albaity, H.; Alsaeed, D.; Al-Turaiki, I. Analyzing Passive BCI Signals to Control Adaptive Automation Devices. Sensors 2019, 19, 3042.

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