Scopus
YÖKSİS DOI Eşleşti
SJR Q3
Glossokinetic potential based tongue–machine interface for 1-D extraction
Australasian Physical and Engineering Sciences in Medicine · Haziran 2018
YÖKSİS Kayıtları
Glossokinetic potential based tongue–machine interface for 1-D extraction
Australasian Physical Engineering Sciences in Medicine · 2018 SCI-Expanded
Prof. Dr. MUHAMMET SERDAR BAŞÇIL →
YÖKSİS Kayıtları — ISSN Eşleşmesi
Multi channel EEG signal feature extraction and pattern recognition on horizontal mental imagination task of 1 D cursor movement for brain computer interface
2015 ISSN: 0158-9938 SCI-Expanded
Prof. Dr. MUHAMMET SERDAR BAŞÇIL →
Spectral feature extraction of EEG signals and pattern recognition during mental tasks of 2 D cursor movements for BCI using SVM and ANN
2016 ISSN: 0158-9938 SCI-Expanded
Prof. Dr. MUHAMMET SERDAR BAŞÇIL →
Glossokinetic potential based tongue–machine interface for 1-D extraction
2018 ISSN: 0158-9938 SCI-Expanded
Prof. Dr. MUHAMMET SERDAR BAŞÇIL →
Makale Bilgileri
ISSN01589938
Yayın TarihiHaziran 2018
Cilt / Sayfa41 · 379-391
Scopus ID2-s2.0-85045102238
Özet
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.
Yazarlar (4)
1
Kutlucan Gorur
2
Mehmet Recep Bozkurt
3
Muhammet Serdar Bascil
4
Feyzullah Temurtas
Anahtar Kelimeler
Assistive technologies
Brain computer interfaces
Electroencephalography
Glossokinetic potential
Tongue machine interfaces
Kurumlar
Bandırma Onyedi Eylül University
Bandirma Turkey
Bozok Üniversitesi
Yozgat Turkey
Sakarya Üniversitesi
Serdivan Turkey
Scimago Dergi (ISSN Eşleşmesi)
Australasian Physical and Engineering Sciences in Medicine
Q3
SJR Skoru0,329
ÜlkeNetherlands
Biomedical Engineering (Q3)
Biophysics (Q3)
Physics and Astronomy (miscellaneous) (Q3)
Radiology, Nuclear Medicine and Imaging (Q3)
Metrikler
12
Atıf
4
Yazar
5
Anahtar Kelime