Spüler, MartinIrastorza-Landa, NereaSarasola-Sanz, AndreaRamos-Murguialday, AnderVilla, Alessandro E.P.Masulli, PaoloRivero, Antonio Javier Pons2024-07-242024-07-242016Spüler , M , Irastorza-Landa , N , Sarasola-Sanz , A & Ramos-Murguialday , A 2016 , Extracting muscle synergy patterns from EMG data using autoencoders . in A E P Villa , P Masulli & A J P Rivero (eds) , Artificial Neural Networks and Machine Learning - 25th International Conference on Artificial Neural Networks, ICANN 2016, Proceedings . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , vol. 9887 LNCS , Springer Verlag , pp. 47-54 , 25th International Conference on Artificial Neural Networks and Machine Learning, ICANN 2016 , Barcelona , Spain , 6/09/16 . https://doi.org/10.1007/978-3-319-44781-0_6conference97833194478030302-9743https://hdl.handle.net/11556/1554Publisher Copyright: © Springer International Publishing Switzerland 2016.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.8enginfo:eu-repo/semantics/restrictedAccessconference output10.1007/978-3-319-44781-0_6Electromyography (EMG)Extensor-flexor musclesMatrix factorizationNeural networkTheoretical Computer ScienceGeneral Computer Sciencehttp://www.scopus.com/inward/record.url?scp=84988411363&partnerID=8YFLogxK