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Glossokinetic potential based tongue-machine interface for 1-D extraction.

Kutlucan Gorur1,2, M Recep Bozkurt1, M Serdar Bascil3

  • 1Department of Electrical and Electronics Engineering, Sakarya University, 54187, Sakarya, Turkey.

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|April 11, 2018
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Summary

This study developed a natural tongue-machine interface using glossokinetic potentials and machine learning. It achieved 99% accuracy, offering a reliable, unobtrusive control method for assistive technologies.

Keywords:
Assistive technologiesBrain computer interfacesElectroencephalographyGlossokinetic potentialTongue machine interfaces

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Area of Science:

  • Biomedical Engineering
  • Human-Computer Interaction
  • Assistive Technology

Background:

  • Traditional tongue-machine interfaces (TMIs) often involve obtrusive hardware, leading to user discomfort and hygiene concerns.
  • Paralyzed individuals require effective and natural control methods for assistive devices.
  • Glossokinetic potentials (GKPs) offer a potential basis for unobtrusive tongue-based control.

Purpose of the Study:

  • To develop a natural and reliable tongue-machine interface (TMI) utilizing solely glossokinetic potentials.
  • To investigate the efficacy of machine learning algorithms for 1-D tongue-based control and communication.
  • To provide an unobtrusive alternative to existing assistive technology control methods.

Main Methods:

  • Eight male and two female healthy subjects (aged 22-34) participated.
  • Glossokinetic potentials were generated by touching buccal walls with the tongue tip.
  • Machine learning algorithms including linear discriminant analysis, support vector machine, and k-nearest neighbor were employed.

Main Results:

  • The support vector machine algorithm achieved the highest success rate.
  • The best participant attained an accuracy of 99% using the support vector machine.
  • The study demonstrated the feasibility of GKP-based TMI for 1-D control.

Conclusions:

  • Glossokinetic potential-based tongue-machine interfaces offer a natural, unobtrusive, speedy, and reliable control method.
  • This GKP-TMI can potentially serve as an alternative to traditional electroencephalography (EEG)-based brain-computer interfaces.
  • The developed TMI holds promise for enhancing the independence and communication abilities of disabled individuals.