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Analyzing Passive BCI Signals to Control Adaptive Automation Devices
Ghada Al-Hudhud1, Layla Alqahtani2, Heyam Albaity3
1Information Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh 12371, Saudi Arabia. galhudhud@ksu.edu.sa.
Sensors (Basel, Switzerland)
|July 13, 2019
Summary
This study introduces an adaptive smart office model using brain computer interfaces to adjust lighting and temperature based on worker comfort and engagement. This enhances assistive technologies and workplace productivity.
Area of Science:
- Assistive Technologies
- Smart Office Environments
- Human-Computer Interaction
Background:
- Brain computer interfaces (BCIs) are pivotal for advancing assistive technologies.
- Smart offices require adaptive systems to optimize user experience and productivity.
- Personalized environmental control can significantly impact worker comfort and engagement.
Purpose of the Study:
- To develop and prototype an adaptive control model for smart offices.
- To integrate electroencephalogram (EEG) signal analysis for assessing worker comfort and engagement.
- To enable automated adjustments of environmental factors like temperature and lighting.
Main Methods:
- Utilized sensors for environmental data (temperature, brightness) and electroencephalogram (EEG) signals.
- Implemented independent component analysis (ICA) for artifact removal from EEG data.
- Developed an engagement index to quantify worker attention and engagement levels.
Main Results:
- Demonstrated a functional prototype for sensing and responding to user comfort and engagement.
- Successfully processed EEG signals to determine worker states.
- Established a system for adaptive environmental control based on user feedback.
Conclusions:
- The adaptive control model enhances smart office functionality through BCIs.
- This research contributes to assistive technologies and smart city infrastructure.
- The system has the potential to increase productivity in smart office settings.
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