Glossokinetic potential based tongue–machine interface for 1-D extraction
- 30 Downloads
The tongue is an aesthetically useful organ located in the oral cavity. It can move in complex ways with very little fatigue. Many studies on assistive technologies operated by tongue are called tongue–human computer interface or tongue–machine interface (TMI) for paralyzed individuals. However, many of them are obtrusive systems consisting of hardware such as sensors and magnetic tracer placed in the mouth and on the tongue. Hence these approaches could be annoying, aesthetically unappealing and unhygienic. In this study, we aimed to develop a natural and reliable tongue–machine interface using solely glossokinetic potentials via investigation of the success of machine learning algorithms for 1-D tongue-based control or communication on assistive technologies. Glossokinetic potential responses are generated by touching the buccal walls with the tip of the tongue. In this study, eight male and two female naive healthy subjects, aged 22–34 years, participated. Linear discriminant analysis, support vector machine, and the k-nearest neighbor were used as machine learning algorithms. Then the greatest success rate was achieved an accuracy of 99% for the best participant in support vector machine. This study may serve disabled people to control assistive devices in natural, unobtrusive, speedy and reliable manner. Moreover, it is expected that GKP-based TMI could be alternative control and communication channel for traditional electroencephalography (EEG)-based brain–computer interfaces which have significant inadequacies arisen from the EEG signals.
KeywordsGlossokinetic potential Tongue machine interfaces Assistive technologies Electroencephalography Brain computer interfaces
The authors would like to thank the students of the University of Bozok for providing the participation for this research.
Compliance with Ethical Standards
Conflict of interest
The authors declare that they have no conflict of interest.
The study was approved by the Ethical Committee of Sakarya University, stated in the number of 61923333/044 decision document. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee.
Informed consent was obtained from all individual participants included in the study.
- 7.Vaidyanathan R, Gupta L, Kook H, West J (2006) A decision fusion classification architecture for mapping of tongue movements based on aural flow monitoring. In: IEEE international conference on robotics and automation. pp. 3610–3617Google Scholar
- 12.Nam Y, Bonkon K, Choi S (2014) Language-related glossokinetic potentials on scalp. IEEE international conference on systems, man, and cybernetics, San Diego, USA. pp. 1063–1067Google Scholar
- 15.Klem GH, Lüders HO, Jasper HH, Elger C (1999) The ten-twenty electrode system of the international federation. Electroencephalogr Clin Neurophysiol 52:3–6Google Scholar
- 18.Alpaydın E (2010) Introduction to machine learning. MIT Press, CambridgeGoogle Scholar
- 23.Shannon CE, Weaver W (1964) Mathematical theory of communication champaign. University of Illinois Press, IllinoisGoogle Scholar
- 24.Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297Google Scholar
- 26.Chang CB, Seo BH (2000) Development of new brain computer interface based on EEG and EMG. In: Proceedings of the IEEE international conference on robotics and biomimetics, Thailand. pp. 1665–1670Google Scholar
- 31.Barreto AB, Taberner AM, Vicente LM. (1996) Classification of spatio-temporal EEG readiness potentials towards the development of a brain-computer interface, bringing together education, science and technology. In: Proceedings of the IEEE, Tampa, FL, USA. pp. 99–102Google Scholar
- 33.Bao X, Wang J, Hu J (2009) Method of individual identification based on electroencephalogram analysis. In: International conference on new trends in information and service science, 2009, NISS’09, (pp. 390–393). IEEE.Google Scholar