Abstract:
Electromyography (EMG) in a bio-driven system is used as a control signal, for driving a hand prosthesis or other wearable
assistive devices. Processing to get informative drive signals involves three main modules: preprocessing, dimensionality
reduction, and classification. This paper proposes a system for classifying a six-channel EMG signal from 14 finger movements.
A feature vector of 66 elements was determined from the six-channel EMG signal for each finger movement. Subsequently,
various feature extraction techniques and classifiers were tested and evaluated. We compared the performance of six feature
extraction techniques, namely principal component analysis (PCA), linear discriminant analysis (LDA), uncorrelated linear
discriminant analysis (ULDA), orthogonal fuzzy neighborhood discriminant analysis (OFNDA), spectral regression linear
discriminant analysis (SRLDA), and spectral regression extreme learning machine (SRELM). In addition, we also evaluated
the performance of seven classifiers consisting of support vector machine (SVM), linear classifier (LC), naive Bayes (NB),
k-nearest neighbors (KNN), radial basis function extreme learning machine (RBF-ELM), adaptive wavelet extreme learning
machine (AW-ELM), and neural network (NN). The results showed that the combination of SRELM as the feature extraction
technique and NN as the classifier yielded the best classification accuracy of 99%, which was significantly higher than those from
the other combinations tested.
