Multivariate Bayesian classification of tongue movement ear pressure signals based on the wavelet packet transform

Tongue movement ear pressure signals have been used to generate controlling commands in human-machine interfaces. The objective of this study is to classify the controlled movement relating to an intended action from interfering signals that can be experienced. These interfering signals include but are not limited to, speech, coughing and drinking. Thus data was collected for six types of controlled movement and the various interfering signals, when subjects spoke, coughed or drank. The signal processing involves detection, segmentation, feature extraction and selection, and classification of tongue motions. The segmented signals were initially transformed into the wavelet packet domain, allowing for various features to be extracted based on statistical properties of the wavelet coefficients. These are then used as input into a Bayesian classifier under multivariate Gaussian assumptions. The average classification performance for identifying controlled movements and interfering tongue signals achieved 98% and 93.5% respectively. Thus the classification of tongue movement ear pressure signals based on the wavelet packet transform is robust. The application of this Bayesian classification strategy significantly reduces the interference of controlling commands when considered within a human-machine interface system operating in a challenging environment.