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A Prototype of EEG System for IoT.

Francisco Laport1, Adriana Dapena1, Paula M Castro1

  • 1Department of Computer Engineering, CITIC Research Center & University of A Coruña, Campus de Elviña, A Coruña 15071, Spain.

International Journal of Neural Systems
|May 5, 2020
PubMed
Summary
This summary is machine-generated.

We created low-cost, open-source hardware and software for classifying eye states (open eyes and closed eyes) using a novel signal processing method, suitable for Internet of Things integration.

Keywords:
ElectroencephalographyInternet of Thingsprototypesignal processing

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Internet of Things (IoT)

Background:

  • Accurate eye state classification is crucial for various applications, including human-computer interaction and health monitoring.
  • Existing systems often lack affordability, accessibility, or seamless integration with modern networked environments.

Purpose of the Study:

  • To develop an open-source, low-cost hardware and software solution for real-time eye state classification.
  • To integrate this system with an Internet of Things (IoT) protocol for remote monitoring and data accessibility.
  • To propose and evaluate a novel signal processing technique for distinguishing between open and closed eye states.

Main Methods:

  • Design and construction of a cost-effective hardware platform with minimal components.
  • Development of open-source software for signal acquisition and processing.
  • Implementation of a classification method based on power ratios across different frequency bands of the electroencephalogram (EEG) or similar bio-signal.
  • Comparison of real- and complex-valued transformations coupled with threshold-based and linear discriminant analysis (LDA) decision strategies.

Main Results:

  • The proposed method effectively classifies open eye (oE) and closed eye (cE) states.
  • Simulation results demonstrate high classifier accuracies for the developed algorithms.
  • Analysis of system delays associated with different transformation and decision strategies was performed.

Conclusions:

  • The developed open-source hardware and software provide an accessible and affordable solution for eye state classification.
  • The novel frequency-band power ratio method offers a viable approach for oE/cE detection.
  • Integration with IoT protocols enhances the system's applicability in remote and networked scenarios.