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Surface electromyogram (SEMG) decomposition provides a promising tool fordecoding and understanding neural drive information non-invasively. In contrastto previous SEMG decomposition methods mainly developed in offline conditions,there are few studies on online SEMG decomposition. A novel method for onlinedecomposition of SEMG data is presented using the progressive FastICA peel-off(PFP) algorithm. The online method consists of an offline prework stage and anonline decomposition stage. More specifically, a series of separation vectorsare first initialized by the originally offline version of the PFP algorithmfrom SEMG data recorded in advance. Then they are applied to online SEMG datato extract motor unit spike trains precisely. The performance of the proposedonline SEMG decomposition method was evaluated by both simulation andexperimental approaches. It achieved an online decomposition accuracy of 98.53%when processing simulated SEMG data. For decomposing experimental SEMG data,the proposed online method was able to extract an average of 12.00 +- 3.46 MUsper trial, with a matching rate of 90.38% compared with results from theexpert-guided offline decomposition. Our study provides a valuable way ofonline decomposition of SEMG data with advanced applications in movementcontrol and health.

Online Decomposition of Surface Electromyogram into Individual Motor Unit Activities Using Progressive FastICA Peel-off