Unsupervised layer-wise feature extraction algorithm for surface electromyography based on information theory

Feature extraction is a key task in the processing of surface electromyography (SEMG) signals. Currently, most of the approaches tend to extract features with deep learning methods, and show great performance. And with the development of deep learning, in which supervised learning is limited by the excessive expense incurred due to the reliance on labels. Therefore, unsupervised methods are gaining more and more attention. In this study, to better understand the different attribute information in the signal data, we propose an information-based method to learn disentangled feature representation of SEMG signals in an unsupervised manner, named Layer-wise Feature Extraction Algorithm (LFEA). Furthermore, due to the difference in the level of attribute abstraction, we specifically designed the layer-wise network structure. In TC score and MIG metric, our method shows the best performance in disentanglement, which is 6.2 lower and 0.11 higher than the second place, respectively. And LFEA also get at least 5.8% accuracy lead than other models in classifying motions. All experiments demonstrate the effectiveness of LEFA.