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Open Software/Hardware Platform for Human-Computer Interface Based on Electrooculography (EOG) Signal Classification.

Jayro Martínez-Cerveró1, Majid Khalili Ardali1, Andres Jaramillo-Gonzalez1

  • 1Institute of Medical Psychology and Behavioural Neurobiology, University of Tübingen, Silcherstraße 5, 72076 Tübingen, Germany.

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|April 30, 2020
PubMed
Summary
This summary is machine-generated.

This study presents an affordable, open-source system using electrooculography (EOG) signals for classifying eye movements. The system achieves 90% accuracy, enabling new possibilities for human-computer interfaces (HCI).

Keywords:
Human-Computer Interface (HCI)Support Vector Machine (SVM)electrooculography (EOG)

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Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Computer Science

Background:

  • Electrooculography (EOG) signals are crucial for Human-Computer Interfaces (HCI).
  • Existing HCI systems often use restrictive, closed environments, limiting user accessibility and applications.
  • There is a need for cost-effective, portable, and adaptable EOG-based HCI solutions.

Purpose of the Study:

  • To develop and evaluate an open-source system for classifying four directions of eye movements using EOG signals.
  • To create a low-cost, compact, and easily replicable EOG-based HCI system.
  • To demonstrate the feasibility of using common open-source hardware and software for biosignal processing.

Main Methods:

  • The system utilizes Raspberry Pi, OpenBCI for biosignal acquisition, and an open-source Python library.
  • EOG signals were processed using Maximum, Minimum, and Median trial values as features.
  • A Support Vector Machine (SVM) classifier was trained and implemented for real-time eye movement classification.

Main Results:

  • The developed system achieved a mean accuracy of 90% in classifying Up, Down, Left, and Right eye movements.
  • Successful online classification was demonstrated in 7 out of 10 subjects.
  • The system proved to be cheap, compact, and portable, suitable for replication and modification.

Conclusions:

  • The open-source EOG-based system offers a viable and accessible solution for eye movement classification.
  • This technology has significant potential for enhancing human-computer interfaces, particularly for individuals with paralysis.
  • The study highlights the effectiveness of combining affordable hardware with open-source software for advanced biosignal applications.