A decomposition algorithm for surface electrode-array electromyogram

García, Gonzalo A.; Okuno, Ryuhei; Azakawa, K.

doi:10.1109/memb.2005.1463398

Cited by 46 publications

(24 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For example, Nakamura et al applied FastICA to extract MUAP trains from electrode array EMG signals [31] [32]. Different ICA algorithms have been tested for separating the MUAP trains [33–35]. Unfortunately, previous ICA-based methods demonstrated limited success for surface EMG decomposition, because the primary effect of ICA processing appeared to be increasing the sparseness of the signal, rather than separating the activity from single motor units (see a recent review [39]).…”

Section: Discussionmentioning

confidence: 99%

A Novel Framework Based on FastICA for High Density Surface EMG Decomposition

Chen

Zhou

2016

IEEE Trans. Neural Syst. Rehabil. Eng.

187

147

View full text Add to dashboard Cite

This study presents a progressive FastICA peel-off (PFP) framework for high density surface electromyogram (EMG) decomposition. The novel framework is based on a shift-invariant model for describing surface EMG. The decomposition process can be viewed as progressively expanding the set of motor unit spike trains, which is primarily based on FastICA. To overcome the local convergence of FastICA, a “peel off” strategy (i.e. removal of the estimated motor unit action potential (MUAP) trains from the previous step) is used to mitigate the effects of the already identified motor units, so more motor units can be extracted. Moreover, a constrained FastICA is applied to assess the extracted spike trains and correct possible erroneous or missed spikes. These procedures work together to improve the decomposition performance. The proposed framework was validated using simulated surface EMG signals with different motor unit numbers (30, 70, 91) and signal to noise ratios (SNRs) (20, 10, 0 dB). The results demonstrated relatively large numbers of extracted motor units and high accuracies (high F1-scores). The framework was also tested with 111 trials of 64-channel electrode array experimental surface EMG signals during the first dorsal interosseous (FDI) muscle contraction at different intensities. On average 14.1 ± 5.0 motor units were identified from each trial of experimental surface EMG signals.

show abstract

Section: Discussionmentioning

confidence: 99%

A Novel Framework Based on FastICA for High Density Surface EMG Decomposition

Chen

Zhou

2016

IEEE Trans. Neural Syst. Rehabil. Eng.

187

147

View full text Add to dashboard Cite

show abstract

“…JADE's performance is not strongly affected by added noise. However, inter-channel delay is the main drawback of this method [80]. …”

Section: Emg Signal Processingmentioning

confidence: 99%

Surface Electromyography Signal Processing and Classification Techniques

Chowdhury

Reaz

Ali

et al. 2013

Sensors

757

377

View full text Add to dashboard Cite

Electromyography (EMG) signals are becoming increasingly important in many applications, including clinical/biomedical, prosthesis or rehabilitation devices, human machine interactions, and more. However, noisy EMG signals are the major hurdles to be overcome in order to achieve improved performance in the above applications. Detection, processing and classification analysis in electromyography (EMG) is very desirable because it allows a more standardized and precise evaluation of the neurophysiological, rehabitational and assistive technological findings. This paper reviews two prominent areas; first: the pre-processing method for eliminating possible artifacts via appropriate preparation at the time of recording EMG signals, and second: a brief explanation of the different methods for processing and classifying EMG signals. This study then compares the numerous methods of analyzing EMG signals, in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above.

show abstract

“…Mathematics techniques, such as PCA, can be used to parse the complex data into a small number of components, for example, only two synergies were needed to account for more than 96% of the activation patterns of eleven human arm muscles during free arm motions (Artemiadis and Kyriakopoulos, 2011). Independent component analysis (ICA) was also effective to extract statistically independent muscle activity source signal from their combinations from multi-channel EMG (Nakamura et al, 2004; Garcia et al, 2005) and high-density EMG signals. ICA has been considered superior to PCA in improving force estimation accuracy from EMG (Staudenmann et al, 2007).…”

Section: Methodsmentioning

confidence: 99%

Simultaneous and Continuous Estimation of Shoulder and Elbow Kinematics from Surface EMG Signals

et al. 2017

View full text Add to dashboard Cite

In this paper, we present a simultaneous and continuous kinematics estimation method for multiple DoFs across shoulder and elbow joint. Although simultaneous and continuous kinematics estimation from surface electromyography (EMG) is a feasible way to achieve natural and intuitive human-machine interaction, few works investigated multi-DoF estimation across the significant joints of upper limb, shoulder and elbow joints. This paper evaluates the feasibility to estimate 4-DoF kinematics at shoulder and elbow during coordinated arm movements. Considering the potential applications of this method in exoskeleton, prosthetics and other arm rehabilitation techniques, the estimation performance is presented with different muscle activity decomposition and learning strategies. Principle component analysis (PCA) and independent component analysis (ICA) are respectively employed for EMG mode decomposition with artificial neural network (ANN) for learning the electromechanical association. Four joint angles across shoulder and elbow are simultaneously and continuously estimated from EMG in four coordinated arm movements. By using ICA (PCA) and single ANN, the average estimation accuracy 91.12% (90.23%) is obtained in 70-s intra-cross validation and 87.00% (86.30%) is obtained in 2-min inter-cross validation. This result suggests it is feasible and effective to use ICA (PCA) with single ANN for multi-joint kinematics estimation in variant application conditions.

show abstract

A decomposition algorithm for surface electrode-array electromyogram

Cited by 46 publications

References 34 publications

A Novel Framework Based on FastICA for High Density Surface EMG Decomposition

A Novel Framework Based on FastICA for High Density Surface EMG Decomposition

Surface Electromyography Signal Processing and Classification Techniques

Simultaneous and Continuous Estimation of Shoulder and Elbow Kinematics from Surface EMG Signals

Contact Info

Product

Resources

About