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Robust real-time identification of tongue movement commands from interferences

Robust real-time identification of tongue movement commands from interferences
Robust real-time identification of tongue movement commands from interferences
This study aimed to improve the accuracy and robustness of a real-time assistive human machine interface system by classifying between the controlled movements related tongue-movement ear pressure (TMEP) signals and the interfering signals. The controlled movement TMEP signals were collected during left, right, up, down, flicking and pushing tongue motions. The TMEP signals were processed and classified using detection, segmentation, feature extraction and classification. The segmented signals were decomposed into the time-scale domain using a wavelet packet transform. The variance of the wavelet packet coefficients and its ratio between low-to-high scales were defined as features and the intended tongue movement commands and interfering signals were classified using both a Bayesian and support vector machine (SVM) classifiers for comparison. The average classification accuracy for discriminating between the controlled movements and the interfering signals achieved 97.8% (Bayesian) and 98.5% (SVM). The classifiers were robust remaining at a similar performance level when generalised interferences from all subjects were used. It was shown that the Bayesian classifier performed better than the SVM in a real-time environment. The approach of combining the Bayesian classifier and the wavelet packet transform provides a robust and efficient method for a real-time assistive human machine interface based on tongue-movement ear pressure signals
tongue-movement ear pressure signals, wavelet packet transform, bayesian classifier, human machine interface
0925-2312
83-92
Mamun, Khondaker A.
d659ccd9-1816-4044-92da-347c2f88eb79
Mace, Michael
6edb3b7c-33d4-4db3-8dc4-14ad95ce7b40
Gupta, Lalit
f953dbc4-0f02-4a22-9f09-46d720ae78bd
Verschuur, Carl A.
5e15ee1c-3a44-4dbe-ad43-ec3b50111e41
Lutman, Mark E.
add34340-3241-4346-a668-8f51fdea6692
Stokes, Maria
71730503-70ce-4e67-b7ea-a3e54579717f
Vaidyanathan, Ravi
246a8aa2-1e70-4543-9055-0581ae00f30a
Wang, Shouyan
fa12f1bf-cac9-4118-abdd-9d52f235b05c
Mamun, Khondaker A.
d659ccd9-1816-4044-92da-347c2f88eb79
Mace, Michael
6edb3b7c-33d4-4db3-8dc4-14ad95ce7b40
Gupta, Lalit
f953dbc4-0f02-4a22-9f09-46d720ae78bd
Verschuur, Carl A.
5e15ee1c-3a44-4dbe-ad43-ec3b50111e41
Lutman, Mark E.
add34340-3241-4346-a668-8f51fdea6692
Stokes, Maria
71730503-70ce-4e67-b7ea-a3e54579717f
Vaidyanathan, Ravi
246a8aa2-1e70-4543-9055-0581ae00f30a
Wang, Shouyan
fa12f1bf-cac9-4118-abdd-9d52f235b05c

Mamun, Khondaker A., Mace, Michael, Gupta, Lalit, Verschuur, Carl A., Lutman, Mark E., Stokes, Maria, Vaidyanathan, Ravi and Wang, Shouyan (2012) Robust real-time identification of tongue movement commands from interferences. [in special issue: Machine Learning for Signal Processing 2010] Neurocomputing, 80, 83-92. (doi:10.1016/j.neucom.2011.09.018).

Record type: Article

Abstract

This study aimed to improve the accuracy and robustness of a real-time assistive human machine interface system by classifying between the controlled movements related tongue-movement ear pressure (TMEP) signals and the interfering signals. The controlled movement TMEP signals were collected during left, right, up, down, flicking and pushing tongue motions. The TMEP signals were processed and classified using detection, segmentation, feature extraction and classification. The segmented signals were decomposed into the time-scale domain using a wavelet packet transform. The variance of the wavelet packet coefficients and its ratio between low-to-high scales were defined as features and the intended tongue movement commands and interfering signals were classified using both a Bayesian and support vector machine (SVM) classifiers for comparison. The average classification accuracy for discriminating between the controlled movements and the interfering signals achieved 97.8% (Bayesian) and 98.5% (SVM). The classifiers were robust remaining at a similar performance level when generalised interferences from all subjects were used. It was shown that the Bayesian classifier performed better than the SVM in a real-time environment. The approach of combining the Bayesian classifier and the wavelet packet transform provides a robust and efficient method for a real-time assistive human machine interface based on tongue-movement ear pressure signals

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More information

e-pub ahead of print date: 7 November 2011
Published date: March 2012
Keywords: tongue-movement ear pressure signals, wavelet packet transform, bayesian classifier, human machine interface
Organisations: Human Sciences Group

Identifiers

Local EPrints ID: 210439
URI: http://eprints.soton.ac.uk/id/eprint/210439
ISSN: 0925-2312
PURE UUID: 2fa6a64f-53d5-4706-9940-9ea31cc32803
ORCID for Maria Stokes: ORCID iD orcid.org/0000-0002-4204-0890

Catalogue record

Date deposited: 08 Feb 2012 15:14
Last modified: 09 Jan 2022 03:11

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Contributors

Author: Khondaker A. Mamun
Author: Michael Mace
Author: Lalit Gupta
Author: Mark E. Lutman
Author: Maria Stokes ORCID iD
Author: Ravi Vaidyanathan
Author: Shouyan Wang

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