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Eye Movement Signal Classification for Developing Human-Computer Interface Using Electrooculogram.

M Thilagaraj1, B Dwarakanath2, S Ramkumar3

  • 1Department of Electronics and Instrumentation Engineering, Karpagam College of Engineering, Coimbatore, Tamil Nadu, India.

Journal of Healthcare Engineering
|December 20, 2021
PubMed
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This summary is machine-generated.

This study demonstrates a novel human-computer interface (HCI) for paralyzed individuals, achieving high accuracy in classifying electrooculography (EOG) signals using Elman Recurrent Neural Networks (ERNN). This technology enables control of assistive devices, enhancing independence for those with motor impairments.

Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Computer Science

Background:

  • Human-computer interfaces (HCI) offer vital communication pathways for individuals with paralysis, bypassing traditional motor pathways.
  • Existing HCI methods face challenges in signal processing and classification accuracy, limiting their real-world application for assistive device control.

Purpose of the Study:

  • To evaluate the feasibility of nine human-computer interface (HCI) states using advanced techniques to aid paralyzed individuals.
  • To assess the performance of Elman Recurrent Neural Network (ERNN) and distributed time delay neural network for electrooculography (EOG) signal classification.

Main Methods:

  • Utilized an Analog Digital Instrument T26 with a five-electrode system on twenty voluntary subjects.
  • Applied notch filtering (50 Hz) for noise reduction and convolution theorem for feature extraction.

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  • Classified extracted features using Elman and distributed time delay neural networks, analyzing performance with single-trial analysis and bit transfer rate (BTR).

    • Main Results:

    • Achieved average classification accuracies of 90.82% (ERNN) and 90.56% (distributed time delay network).
    • The ERNN model demonstrated superior potential in classifying, identifying, and recognizing EOG signals compared to the distributed time delay network for most subjects.
    • Generated control signals successfully navigated assistive devices like mice, keyboards, and wheelchairs.

    Conclusions:

    • The developed HCI system, particularly with the ERNN model, shows significant potential for enhancing the independence and quality of life for individuals with paralysis.
    • The study validates the feasibility of using EOG-based HCI for controlling assistive technologies, paving the way for future advancements.