EEG-based brain-computer interface for smart living environmental auto-adjustment

Chin Teng Lin, Fu Chang Lin, Shi An Chen, Shao Wei Lu, Te Chi Chen, Li-Wei Ko*

*Corresponding author for this work

Research output: Contribution to journalArticle

30 Scopus citations

Abstract

An EEG-based smart living environmental control system to auto-adjust the living environment is proposed in this study. Many environmental control systems have been proposed to improve human life quality in recent years. However, there is little research focused on environment control by using a human's physiological state directly. Even though some studies have proposed brain computer interface-based (BCI-based) environmental control systems, most of them encountered signal quality decline during long-term physiological monitoring with conventional wet electrodes. Moreover, such BCI-based environmental control systems are actively controlled by users; less close-loop feedback capability can be provided between environment and user for automation. Based on the advance of our technique for BCI and the improvement of micro-electro- mechanical-system-based (MEMS-based) dry electrode sensors, we combined these techniques to demonstrate an auto-adjustable living environment control system, e.g., illumination of light and fan speed of air conditioner, depends on the physiological change of the user, even for long-term physiological monitoring. The system is structured with five units: a wireless portable EEG acquisition circuit unit, an interactive flow control unit with a real-time physiology signal processing unit, which is implemented on a dual-core processor, an environment controller unit and a host system for data storage and display. The proposed system has been verified in a simulated environment and the experimental results show that the air conditioner and the lights can be successfully and automatically adjusted in real-time based on the subject's physiological changes, which indicate the proposed system can be implemented and constructed in the practical smart living environment or for other applications.

Original languageEnglish
Pages (from-to)237-245
Number of pages9
JournalJournal of Medical and Biological Engineering
Volume30
Issue number4
DOIs
StatePublished - 16 Sep 2010

Keywords

  • Brain computer interface (BCI)
  • Electroencephalogram (EEG)
  • Micro-electro-mechanical-system (MEMS)

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