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Hybrid EEG-fNIRS-Based Eight-Command Decoding for BCI: Application to Quadcopter Control.

Muhammad Jawad Khan1, Keum-Shik Hong2

  • 1School of Mechanical Engineering, Pusan National University , Busan , South Korea.

Frontiers in Neurorobotics
|March 7, 2017
PubMed
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This study presents a hybrid electroencephalography-functional near-infrared spectroscopy (EEG-fNIRS) system to control a quadcopter using eight brain commands. The brain-computer interface achieved high accuracy for real-time control.

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Human-Computer Interaction

Background:

  • Brain-computer interfaces (BCIs) enable device control via neural signals.
  • Hybrid BCIs combining electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) offer enhanced decoding capabilities.
  • Decoding complex commands from the frontal brain region remains a challenge.

Purpose of the Study:

  • To develop and evaluate a hybrid EEG-fNIRS BCI system for decoding eight distinct brain commands.
  • To assess the system's efficacy in controlling a quadcopter in real-time.
  • To investigate the potential of frontal brain activity for complex command generation.

Main Methods:

  • A hybrid EEG-fNIRS system was implemented, with fNIRS targeting the prefrontal cortex and EEG covering frontal, parietal, and visual cortices.
Keywords:
brain–computer interfaceclassificationhybrid EEG–fNIRSmental taskquadcopter control

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  • fNIRS decoded cognitive tasks (mental arithmetic, counting, rotation, word formation) using ΔHbO features.
  • EEG decoded motor imagery and eye movements (eyeblinks, gaze direction) using signal peak and mean features.

    • Main Results:

    • fNIRS achieved 75.6% accuracy in decoding four cognitive tasks.
    • EEG achieved 86% accuracy in decoding four motor imagery and eye movement tasks.
    • The hybrid system successfully controlled a quadcopter in real-time using eight distinct commands.

    Conclusions:

    • The proposed hybrid EEG-fNIRS interface effectively decodes eight brain commands from the frontal cortex.
    • This BCI system demonstrates the feasibility of real-time, online control of external devices like quadcopters.
    • Hybrid BCIs show significant potential for advanced neuroprosthetics and assistive technologies.