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Controlling of smart home system based on brain-computer interface.

Qiang Gao1, Xuewen Zhao1, Xiao Yu1

  • 1Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems, Tianjin University of Technology, Tianjin 300384, China.

Technology and Health Care : Official Journal of the European Society for Engineering and Medicine
|August 15, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a cost-effective brain-computer interface system using steady-state visual evoked potentials (SSVEP) to control smart home devices. The system achieved an 86.88% accuracy rate, offering a new way to support individuals with severe disabilities.

Keywords:
Brain computer interfacePower over Ethernetcanonical correlation analysisdiscrete wavelet transformsmart home systemsteady-state visual evoked potential

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Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Human-Computer Interaction

Background:

  • Brain-computer interface (BCI) technology offers communication and control solutions for individuals with severe disabilities like ALS and paralysis.
  • EEG-based BCI systems aim to enhance the quality of life for those with motor impairments.
  • The proposed Brain-Computer Interface-Based Smart Home System (BCIBSHS) seeks to improve daily living for paralyzed individuals and the elderly.

Purpose of the Study:

  • To establish a cost-effective and safe online BCIBSHS for multi-command recognition.
  • To enable control of smart home devices using SSVEP-based BCI.

Main Methods:

  • Utilized the Emotiv EPOC portable EEG device for signal acquisition.
  • Applied Discrete Wavelet Transform (DWT) for signal denoising and Canonical Correlation Analysis (CCA) for feature extraction and classification.
  • Integrated SSVEP classification results to control smart home devices.

Main Results:

  • Successfully controlled four smart home devices (lamp, web camera, guardianship telephone, intelligent blinds) using distinct commands.
  • Achieved an average classification accuracy rate of 86.88 ± 5.30% in online experiments.
  • Employed Power over Ethernet (PoE) technology for device power and communication.

Conclusions:

  • The developed online SSVEP-based BCI system effectively manages smart home applications using EEG signals.
  • This BCI and PoE integrated system overcomes limitations of traditional assistive technologies.
  • The study demonstrates a viable approach for home application management for individuals with disabilities.