Multi-layer neural network classification of tongue movement ear pressure signal for human machine interface

Tongue movement ear pressure (TMEP) signals have been used to generate controlling commands in assistive human machine interfaces aimed at people with disabilities. The objective of this study is to classify the controlled movement related signals of an intended action from internally occurring physiological signals which can interfere with the inter-movement classification. TMEP signals were collected, corresponding to six types of controlled movements and activity relating to the potentially interfering environment including when a subject spoke, coughed or drank. The signal processing algorithm involved TMEP signal detection, segmentation, feature extraction and selection, and classification. The features of the segmented TMEP signals were extracted using the wavelet packet transform (WPT). A multi-layer neural network was then designed and tested based on statistical properties of the WPT coefficients. The average classification performance for discriminating interference and controlled movement related TMEP signal achieved 97.05%. The classification of TMEP signals based on the WPT is robust and the interferences to the controlling commands of TMEP signals in assistive human machine interface can be significantly reduced using the multi-layer neural network when considered in this challenging environment.