TY - GEN
T1 - Beyond user-specificity for EMG decoding using multiresolution muscle synergy analysis
AU - Ison, Mark R.
AU - Artemiadis, Panagiotis
PY - 2013
Y1 - 2013
N2 - Electromyographic (EMG) processing is a vital step towards converting noisy muscle activation signals into robust features that can be decoded and applied to applications such as prosthetics, exoskeletons, and human-machine interfaces. Current state of the art processing methods involve collecting a dense set of features which are sensitive to many of the intra-and intersubject variability ubiquitous in EMG signals. As a result, state of the art decoding methods have been unable to obtain subject independence. This paper presents a novel multiresolution muscle synergy (MRMS) feature extraction technique which represents a set of EMG signals in a sparse domain robust to the inherent variability of EMG signals. The robust features, which can be extracted in real time, are used to train a neural network and demonstrate a highly accurate and user-independent classifier. Leave-one-out validation testing achieves mean accuracy of 81:9±3:9% and area under the receiver operating characteristic curve (AUC), a measure of overall classifier performance over all possible thresholds, of 92:4±8:9%. The results show the ability of sparse MRMS features to achieve subject independence in decoders, providing opportunities for large-scale studies and more robust EMG-driven applications.
AB - Electromyographic (EMG) processing is a vital step towards converting noisy muscle activation signals into robust features that can be decoded and applied to applications such as prosthetics, exoskeletons, and human-machine interfaces. Current state of the art processing methods involve collecting a dense set of features which are sensitive to many of the intra-and intersubject variability ubiquitous in EMG signals. As a result, state of the art decoding methods have been unable to obtain subject independence. This paper presents a novel multiresolution muscle synergy (MRMS) feature extraction technique which represents a set of EMG signals in a sparse domain robust to the inherent variability of EMG signals. The robust features, which can be extracted in real time, are used to train a neural network and demonstrate a highly accurate and user-independent classifier. Leave-one-out validation testing achieves mean accuracy of 81:9±3:9% and area under the receiver operating characteristic curve (AUC), a measure of overall classifier performance over all possible thresholds, of 92:4±8:9%. The results show the ability of sparse MRMS features to achieve subject independence in decoders, providing opportunities for large-scale studies and more robust EMG-driven applications.
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U2 - 10.1115/DSCC2013-4070
DO - 10.1115/DSCC2013-4070
M3 - Conference contribution
AN - SCOPUS:84902468415
SN - 9780791856123
T3 - ASME 2013 Dynamic Systems and Control Conference, DSCC 2013
BT - Aerial Vehicles; Aerospace Control; Alternative Energy; Automotive Control Systems; Battery Systems; Beams and Flexible Structures; Biologically-Inspired Control and its Applications;
PB - American Society of Mechanical Engineers (ASME)
T2 - ASME 2013 Dynamic Systems and Control Conference, DSCC 2013
Y2 - 21 October 2013 through 23 October 2013
ER -