Glossokinetic potential based tongue–machine interface for 1-D extraction using neural networks

• Autorzy: Gorur K. , Bozkurt M. R. , Bascil M. S. , Temurtas F.

Identyfikatory

DOI

10.1016/j.bbe.2018.06.004

• Języki publikacji: EN

Abstrakty

EN

Tongue machine interface (TMI) is a tongue-operated assistive technology enabling people with severe disabilities to control their environments using their tongue motion. In many disorders such as amyotrophic lateral sclerosis or stroke, people can communicate with the external world in a limited degree. However, they may be disabled, while their mind is still intact. Various tongue–machine interface techniques has been developed to support these people by providing additional communication pathway. In this study, we aimed to develop a tongue–machine interface approach by investigating pattern of glossokinetic potential (GKP) signals using neural networks via simple right/left tongue touchings to the buccal walls for 1-D control and communication, named as GKP-based TMI. As can be known in the literature, the tongue is connected to the brain via hypoglossal cranial nerve. Therefore, it generally escapes from the severe damages, in spinal cord injuries and was slowly affected than limbs of persons suffering from many neuromuscular degenerative disorders. In this work, 8 male and 2 female naive healthy subjects, aged 22 to 34 years, participated. Multilayer neural network and probabilistic neural network were employed as classification algorithms with root-mean-square and power spectral density feature extraction operations. Then the greatest success rate achieved was 97.25%. 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 a collaboration channel for traditional electroencephalography (EEG)-based brain computer interfaces which have significant inadequacies arisen from the EEG signals.

Słowa kluczowe

EN

glossokinetic potential; assistive technologies; multilayer neural network; probabilistic neural network

PL

technologia asystująca; sieć neuronowa wielowarstwowa; probabilistyczna sieć neuronowa

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2018
• Tom: Vol. 38, no. 3
• Strony: 745--759
• Opis fizyczny: Bibliogr. 55 poz., rys., tab., wykr.

Twórcy

autor

Gorur K.

• Department of Electrical and Electronics Engineering, Sakarya University, Sakarya, Turkey; Department of Electrical and Electronics Engineering, Bozok University, 66200 Yozgat, Turkey

autor

Bozkurt M. R.

• Department of Electrical and Electronics Engineering, Sakarya University, Sakarya, Turkey

autor

Bascil M. S.

• Department of Electrical and Electronics Engineering, Bozok University, Yozgat, Turkey

autor

Temurtas F.

• Department of Electrical and Communication Engineering, Bandirma Onyedi Eylul University, Balikesir, Turkey

Bibliografia

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Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).