Comparative Evaluation for PCA and ICA on Tongue-Machine Interface Using Glossokinetic Potential Responses

Year 2020, Volume: 16 Issue: 1, 35 - 46, 27.03.2020

Kutlucan Görür , Mehmet Recep Bozkurt , Muhammed Serdar Başçıl Feyzullah Temurtas

Abstract

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The tongue-machine interface (TMI)
between the paralyzed person and computer makes possible to manage assistive
technologies. Severely disabled individuals caused by traumatic brain and
spinal cord injuries need continuous help to carry out everyday routines. The
cranial nerve is arisen directly from the brain to connect the tongue that is
one of the last affected organs in neuromuscular disorders. Besides, the tongue has highly
capable of mobility located in the oral cavity which also provides cosmetic
advantages. These crucial skills make the tongue to be an odd organ employed in
the human-machine interfaces. In this study, it was aimed to investigate 1-D
extraction and develop a novel tongue-machine interface using the glossokinetic
potential responses (GKPs). This rare used bio-signs are occurred by contacting
the buccal walls with the tip of the tongue in the oral cavity. Our study,
named as GKP-based TMI measuring the glossokinetic potential responses over the
scalp may serve paralyzed persons an unobtrusive, natural and reliable
communication channel. In this work, 8 males and 2 females, aged between 22-34
naive healthy subjects have participated. Linear discriminant analysis and
support vector machine were implemented with mean-absolute value and power
spectral density feature extraction process. Moreover independent component
analysis (ICA) and principal component analysis (PCA) were used to evaluate the
reduced dimension of the data set for GKPs in machine learning algorithms. And
the highest result was obtained as 97.03%. 

Keywords

Assistive Technologies, Glossokinetic Potential Responses, Independent Component Analysis, Principal Component Analysis, Tongue-Machine Interface

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