Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Feature selection with neural networks
Pattern Recognition Letters
Eigenvector-Based Feature Extraction for Classification
Proceedings of the Fifteenth International Florida Artificial Intelligence Research Society Conference
A log-linearized Gaussian mixture network and its application toEEG pattern classification
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Bayes-optimality motivated linear and multilayered perceptron-based dimensionality reduction
IEEE Transactions on Neural Networks
Computers in Biology and Medicine
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Feature extraction is an important issue in electromyography (EMG) pattern classification, where feature sets of high dimensionality are always used. This paper proposes a novel classification method to deal with high-dimensional EMG patterns, using a probabilistic neural network, a reduced-dimensional log-linearized Gaussian mixture network (RD-LLGMN) [1]. Since RD-LLGMN merges feature extraction and pattern classification processes into its structure, lower-dimensional feature set consistent with classification purposes can be extracted, so that, better classification performance is possible. To verify feasibility of the proposed method, phoneme classification experiments were conducted using frequency features of EMG signals measured from mimetic and cervical muscles. Filter banks are used to extract frequency features, and dimensionality of the features grows significantly when we increase resolution of frequency. In these experiments, the proposed method achieved considerably high classification rates, and outperformed traditional methods that are based on principle component analysis (PCA).