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A New Approach on HCI Extracting Conscious Jaw Movements Based on EEG Signals Using Machine Learnings.

M Serdar Bascil1

  • 1Department of Electrical and Electronics Engineering, Bozok University, 66200, Yozgat, Turkey. serdar.bascil@bozok.edu.tr.

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|August 6, 2018
PubMed
Summary

This study introduces a novel jaw machine interface (JMI) for individuals with paralysis. Brain signals from jaw movements are translated into machine commands, offering new assistive technology possibilities.

Keywords:
EEGFeature extractionJaw machine interface (JMI)Machine learning

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Assistive Technology

Background:

  • Machine computer interfaces (MCI) are crucial for communication and environmental control for paralyzed individuals.
  • Existing MCI technologies have limitations in providing intuitive and accessible control methods.

Purpose of the Study:

  • To develop and evaluate a new MCI approach utilizing voluntary jaw movements recorded via electroencephalogram (EEG).
  • To establish a "jaw machine interface" (JMI) for enhanced environmental control for paralyzed users.

Main Methods:

  • Extraction of electroencephalogram (EEG) signals corresponding to voluntary jaw movements.
  • Feature extraction using root mean square (RMS) and standard deviation (STD).
  • Application of principle component analysis (PCA) for dimensionality reduction, followed by linear discriminant analysis (LDA) and support vector machine (SVM) with k-fold cross-validation for pattern recognition.

Main Results:

  • Successful identification of distinct EEG patterns associated with left/right jaw movements.
  • Demonstration of the feasibility of translating jaw-induced EEG signals into machine control commands.
  • Validation of the JMI system's potential for practical application in assistive technology.

Conclusions:

  • The proposed jaw machine interface (JMI) offers a promising new avenue for assistive technology, enabling paralyzed individuals to control devices using jaw movements.
  • EEG-based analysis of jaw movements provides a viable method for developing intuitive and effective MCI systems.
  • Further research can explore real-time implementation and integration with various assistive devices.