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        Robust real-time identification of tongue movement commands from interferences

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        Author
        Mamun, Khondaker A.
        Mace, Michael
        Gupta, Lalit
        Verschuur, Carl A.
        Lutman, Mark E.
        Stokes, Maria
        Vaidyanathan, Ravi
        Wang, Shouyan
        Date
        2011
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        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.
        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.
        URI
        http://hdl.handle.net/10945/61130
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