Robust real-time identification of tongue movement commands from interferences
Author
Mamun, Khondaker A.
Mace, Michael
Gupta, Lalit
Verschuur, Carl A.
Lutman, Mark E.
Stokes, Maria
Vaidyanathan, Ravi
Wang, Shouyan
Date
2011Metadata
Show full item recordAbstract
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.
Description
The article of record as published may be found at http://dx.doi.org/10.1016/j.neucom.2011.09.018
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.Collections
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