  Surface electromyogram (SEMG) decomposition provides a promising tool for
decoding and understanding neural drive information non-invasively. In contrast
to previous SEMG decomposition methods mainly developed in offline conditions,
there are few studies on online SEMG decomposition. A novel method for online
decomposition of SEMG data is presented using the progressive FastICA peel-off
(PFP) algorithm. The online method consists of an offline prework stage and an
online decomposition stage. More specifically, a series of separation vectors
are first initialized by the originally offline version of the PFP algorithm
from SEMG data recorded in advance. Then they are applied to online SEMG data
to extract motor unit spike trains precisely. The performance of the proposed
online SEMG decomposition method was evaluated by both simulation and
experimental 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 MUs
per trial, with a matching rate of 90.38% compared with results from the
expert-guided offline decomposition. Our study provides a valuable way of
online decomposition of SEMG data with advanced applications in movement
control and health.
