A decision fusion classification architecture for mapping of tongue movements based on aural flow monitoring

Vaidyanathan, Ravi; Gupta, Lalit; Kook, Hyunseok; West, J.

doi:10.1109/robot.2006.1642253

Cited by 5 publications

(5 citation statements)

References 8 publications

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“…Vaidyanathan et al have developed the approach of the airflow pressure changes due to the tongue movements in the ear canal. Although the classification accuracy of 97 % was achieved with decision fusion classification algorithm in the study, the ear listening performances and comforts might be degraded for the patients due to the microphone attached in the ear canal [17][18][19]. However, in our study, glossokinetic potential based approach has simple tongue contacts on the buccal walls and does not affect listening fulfillments.…”

Section: Introductionmentioning

confidence: 62%

GKP Signal Processing Using Deep CNN and SVM for Tongue-Machine Interface

Gorur¹,

Bozkurt²,

Bascil³

et al. 2019

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The tongue is one of the few organs with high mobility in the case of severe spinal cord injuries. However, most tongue-machine interfaces (TMIs) require the patient to wear obtrusive and unhygienic devices in and around the mouth. This paper aims to develop a TMI based on the glossokinetic potentials (GKPs), i.e. the electrical signals generated by the tongue when it touches the buccal walls. Ten participants were recruited for this research. The GKP patterns were classified by convolutional neural network (CNN) and support vector machine (SVM). It was observed that the CNN outperformed the SVM in individual and average scores for both raw and preprocessed datasets, reaching an accuracy of 97~99%. The CNN-based GKP processing method makes it easy to build a natural, appealing and robust TMI for the paralyzed. Being the first attempt to process GKPs with the CNN, our research offers an alternative to the traditional brain-computer interfaces (BCIs), which suffers from the instability and low signalto-noise ratio (SNR) of electroencephalography (EEG).

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Section: Introductionmentioning

confidence: 62%

GKP Signal Processing Using Deep CNN and SVM for Tongue-Machine Interface

Gorur¹,

Bozkurt²,

Bascil³

et al. 2019

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show abstract

“…However, our study, glossokinetic potential responses on a tongue-machine interface may offer handicapped people in a natural and easy-to-use control in assistive devices. The other design approach of a TMI by Vaidyanathan et al is based on airflow pressure changes created by the tongue movements, therefore attaching a microphone to the air canal [18][19][20][21][22]. However, GKP-based TMI can handle to manage an AT without affecting listening performance due to the acquisition the signals over the scalp.…”

Section: Introductionmentioning

confidence: 99%

Tongue-Operated Biosignal over EEG and Processing with Decision Tree and kNN

Gorur¹,

Bozkurt²,

Bascil³

et al. 2021

Academic Platform Journal of Engineering and Science

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Tongue-machine interface (TMI) is a feasible way between the assistive technologies and paralyzed individuals who have lost their abilities to communicate with the environment. Researchers have presented equipment based tongue-machine interfaces to reach a reliable and speedy system. However, this kind of interfaces may occur a way of obtrusive, unattractive and unhygienic for disabled persons. In this research, we intended to propose a natural, unobtrusive and robust glossokinetic potential signals (GKP) based TMI exploring the success of the novel machine learning algorithms. The tongue is bound up with cranial nerves to the brain, which can escape from the spinal cord injuries in general. Moreover, the tongue has highly capable of sophisticated manipulation tasks with less perceived exertion in the oral cavity and gives degrees of privacy. In this study, ten naive healthy subjects have attended who were between 22-34 ages. Decision Tree (DT) and k-Nearest Neighbors (kNN) algorithms were used with Mean-Absolute Value (MAV) and Power Spectral Density (PSD) methods. Moreover, Discrete Wavelet Transform (DWT) was implemented to reveal the theta and delta subbands. In the study, the highest value was provided as 96.77% by the k-Nearest Neighbor algorithm for the best participant. Furthermore, the GKP-based TMI may be an alternative system for the limitations of the brain-computer interfaces. It is well-known that EEG deficits are major concerns for brain-computer interfaces.

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“…Specifically, we have introduced a non-intrusive tongue-movement HMI concept [1][2][3][4][5][6], and shown tongue movements within the oral cavity create unique pressure signals in the ear (dubbed tongue-movement-ear-pressure (TMEP) signals). We have further developed and implemented new pattern classification strategies that have accurately recognized TMEP signals with over 97% accuracy over a range of users [3], hence providing an unobtrusive, completely noninvasive method of controlling peripheral or assist mechanisms through tongue movement.…”

Section: Introductionmentioning

confidence: 99%

A wavelet denoising approach for signal action isolation in the ear canal

Vaidyanathan

Wang

Gupta³

2008

2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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The goal of this work was to develop and implement a new filtering strategy to denoise acoustic signals in the ear canal resulting from voluntary movement of the tongue (as a method of generating control input), as well as from other active actions, (speech, eating, drinking, smoking), and passive actions (swallowing, adjusting the jaw, physiological activity). The strategy is based on a denoising wavelet shrinkage approach that separates rhythmic bursting activity and white noise representing sustained tonic activity. While past work has addressed the discrimination of voluntary TMEP signals from one-another, no work has addressed acoustic artefact rejection within the ear. The results described here, combined with our past work in isolating critical components of tongue movement ear pressure (TMEP) signals, provide a basis for discriminating voluntary and involuntary actions of the tongue by monitoring pressure in the ear. At this time, the system has worked in real-time for assistive device control.

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A decision fusion classification architecture for mapping of tongue movements based on aural flow monitoring

Cited by 5 publications

References 8 publications

GKP Signal Processing Using Deep CNN and SVM for Tongue-Machine Interface

GKP Signal Processing Using Deep CNN and SVM for Tongue-Machine Interface

Tongue-Operated Biosignal over EEG and Processing with Decision Tree and kNN

A wavelet denoising approach for signal action isolation in the ear canal

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