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Recording Horizontal Saccade Performances Accurately in Neurological Patients Using Electro-oculogram
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An electrooculogram-based binary saccade sequence classification (BSSC) technique for augmentative communication and

Johnalan Keegan1, Edward Burke, James Condron

  • 1School of Electrical Engineering Systems, Dublin Institute of Technology, Kevin St., Dublin 8, Ireland. johnalan.keegan@dit.ie

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|December 8, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a new assistive technology using electrooculogram (EOG) signals to control devices. It decodes sequences of saccadic eye movements for communication and control, aiding individuals with severe motor disabilities.

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Area of Science:

  • Assistive Technology
  • Human-Computer Interaction
  • Biomedical Engineering

Background:

  • Individuals with severe motor disabilities often require assistance for daily tasks.
  • The electrooculogram (EOG) offers a potential communication channel and man-machine interface.
  • Detecting and classifying eye movements can enable new control paradigms.

Purpose of the Study:

  • To develop and evaluate a method for detecting saccadic eye movements.
  • To implement a saccade sequence classification algorithm for communication and control.
  • To assess the system's effectiveness and extensibility for assistive purposes.

Main Methods:

  • Utilized saccadic eye movement detection and classification based on pre-defined visual templates.
  • Employed a coordinate transformation algorithm to account for user position and orientation changes.
  • Tested the system with two subjects performing 8 distinct command sequences.

Main Results:

  • Successfully translated sequences of saccades into executable commands.
  • Demonstrated the system's ability to recognize pre-defined saccade sequences.
  • Preliminary experiments showed feasibility with human subjects.

Conclusions:

  • The developed EOG-based system provides a viable method for communication and control for individuals with motor impairments.
  • The saccade sequence classification algorithm, combined with visual templates, offers a robust control mechanism.
  • The system is extensible for applications like text entry, enhancing its utility.