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This paper evaluates supervised and unsupervised adaptive schemes applied to online support vector machine (SVM) that classifies BCI data. Online SVM processes fresh samples as they come and update existing support vectors without referring to pervious samples. It is shown that the performance of online SVM is similar to that of the standard SVM, and both supervised and unsupervised schemes improve...
This paper presents a novel method to classify human facial movement based on multi-channel forehead bio-signals. Five face movements form three face regions: forehead, eye and jaw are selected and classified in back propagation artificial neural networks (BPANN) by using a combination of transient and steady features from EMG and EOG waveforms. The identified face movements are subsequently employed...
This paper proposes and evaluates the application of support vector machine (SVM) to classify upper limb motions using myoelectric signals. It explores the optimum configuration of SVM-based myoelectric control, by suggesting an advantageous data segmentation technique, feature set, model selection approach for SVM, and postprocessing methods. This work presents a method to adjust SVM parameters before...
This paper presents a novel support vector machine (SVM) approach to upper limb motion classification using myoelectric signals. The main purpose of this paper is to compare SVM-based classifiers with LDA and MLP. SVM demonstrates exceptional classification accuracy and results in a robust way of limb motion classification with low computational cost. The validity of entropy, as an index to measure...
This paper presents an ongoing investigation to select optimal subset of features from set of well-known myoelectric signals (MES) features in time and frequency domains. Four channel of myoelectric signal from upper limb muscles are used in this paper to classify six distinctive activities. Cascaded genetic algorithm (GA) has been adopted as the search strategy in feature subset selection. Davies-Bouldin...
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