Local discriminant bases and their applications
Journal of Mathematical Imaging and Vision - Special issue on mathematical imaging
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
ECG beats classification using multiclass support vector machines with error correcting output codes
Digital Signal Processing
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Tongue-Movement Communication and Control Concept for Hands-Free Human–Machine Interfaces
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Entropy-based algorithms for best basis selection
IEEE Transactions on Information Theory - Part 2
Hi-index | 0.01 |
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.