TY - GEN
T1 - Extracting muscle synergy patterns from EMG data using autoencoders
AU - Spüler, Martin
AU - Irastorza-Landa, Nerea
AU - Sarasola-Sanz, Andrea
AU - Ramos-Murguialday, Ander
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2016.
PY - 2016
Y1 - 2016
N2 - Muscle synergies can be seen as fundamental building blocks of motor control. Extracting muscle synergies from EMG data is a widely used method in motor related research. Due to the linear nature of the methods commonly used for extracting muscle synergies, those methods fail to represent agonist-antagonist muscle relationships in the extracted synergies. In this paper, we propose to use a special type of neural networks, called autoencoders, for extracting muscle synergies. Using simulated data and real EMG data, we show that autoencoders, contrary to commonly used methods, allow to capture agonist-antagonist muscle relationships, and that the autoencoder models have a significantly better fit to the data than others methods.
AB - Muscle synergies can be seen as fundamental building blocks of motor control. Extracting muscle synergies from EMG data is a widely used method in motor related research. Due to the linear nature of the methods commonly used for extracting muscle synergies, those methods fail to represent agonist-antagonist muscle relationships in the extracted synergies. In this paper, we propose to use a special type of neural networks, called autoencoders, for extracting muscle synergies. Using simulated data and real EMG data, we show that autoencoders, contrary to commonly used methods, allow to capture agonist-antagonist muscle relationships, and that the autoencoder models have a significantly better fit to the data than others methods.
KW - Electromyography (EMG)
KW - Extensor-flexor muscles
KW - Matrix factorization
KW - Neural network
UR - http://www.scopus.com/inward/record.url?scp=84988411363&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-44781-0_6
DO - 10.1007/978-3-319-44781-0_6
M3 - Conference contribution
AN - SCOPUS:84988411363
SN - 9783319447803
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 47
EP - 54
BT - Artificial Neural Networks and Machine Learning - 25th International Conference on Artificial Neural Networks, ICANN 2016, Proceedings
A2 - Villa, Alessandro E.P.
A2 - Masulli, Paolo
A2 - Rivero, Antonio Javier Pons
PB - Springer Verlag
T2 - 25th International Conference on Artificial Neural Networks and Machine Learning, ICANN 2016
Y2 - 6 September 2016 through 9 September 2016
ER -