Sparsity-based algorithms for blind separation of convolutive mixtures with application to EMG signals

Boudjellal, Abdelouahab; Abed-Meraim, Karim; Bey, Abdeldjalil Aïssa El; Belouchrani, Adel; Ravier, Philippe

doi:10.1109/ssp.2014.6884607

Cited by 2 publications

(1 citation statement)

References 8 publications

(11 reference statements)

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“…Many features, including independence component analysis, root-mean-square, distance-based features, and nonlinear multiscale features have been suggested [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. Various machine learning algorithms, such as fuzzy, support vector machines (SVM), and neural networks were applied to solve the classification matter [7,9,20,21].…”

Section: Introductionmentioning

confidence: 99%

EMG Pattern Classification by Split and Merge Deep Belief Network

Shim

Lee

et al. 2016

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Abstract:In this paper; we introduce an enhanced electromyography (EMG) pattern recognition algorithm based on a split-and-merge deep belief network (SM-DBN). Generally, it is difficult to classify the EMG features because the EMG signal has nonlinear and time-varying characteristics. Therefore, various machine-learning methods have been applied in several previously published studies. A DBN is a fast greedy learning algorithm that can identify a fairly good set of weights rapidly-even in deep networks with a large number of parameters and many hidden layers. To reduce overfitting and to enhance performance, the adopted optimization method was based on genetic algorithms (GA). As a result, the performance of the SM-DBN was 12.06% higher than conventional DBN. Additionally, SM-DBN results in a short convergence time, thereby reducing the training epoch. It is thus efficient in reducing the risk of overfitting. It is verified that the optimization was improved using GA.

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Section: Introductionmentioning

confidence: 99%

EMG Pattern Classification by Split and Merge Deep Belief Network

Shim

Lee

et al. 2016

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A sparse Bayesian learning based scheme for multi-movement recognition using sEMG

Ding

Wang

2015

Australas Phys Eng Sci Med

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This paper proposed a feature extraction scheme based on sparse representation considering the non-stationary property of surface electromyography (sEMG). Sparse Bayesian learning was introduced to extract the feature with optimal class separability to improve recognition accuracy of multi-movement patterns. The extracted feature, sparse representation coefficients (SRC), represented time-varying characteristics of sEMG effectively because of the compressibility (or weak sparsity) of the signal in some transformed domains. We investigated the effect of the proposed feature by comparing with other fourteen individual features in offline recognition. The results demonstrated the proposed feature revealed important dynamic information in the sEMG signals. The multi-feature sets formed by the SRC and other single feature yielded more superior performance on recognition accuracy, compared with the single features. The best average recognition accuracy of 94.33% was gained by using SVM classifier with the multi-feature set combining the feature SRC, Williston amplitude (WAMP), wavelength (WL) and the coefficients of the fourth order autoregressive model (ARC4) via multiple kernel learning framework. The proposed feature extraction scheme (known as SRC + WAMP + WL + ARC4) is a promising method for multi-movement recognition with high accuracy.

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Sparsity-based algorithms for blind separation of convolutive mixtures with application to EMG signals

Cited by 2 publications

References 8 publications

EMG Pattern Classification by Split and Merge Deep Belief Network

EMG Pattern Classification by Split and Merge Deep Belief Network

A sparse Bayesian learning based scheme for multi-movement recognition using sEMG

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