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

TL;DR

This paper introduces a novel online SEMG decomposition method using progressive FastICA peel-off, enabling real-time extraction of motor unit activities with high accuracy, facilitating advanced applications in movement control and health monitoring.

Contribution

The paper presents a new online SEMG decomposition approach combining offline initialization with real-time processing using the PFP algorithm, improving speed and accuracy over previous offline-only methods.

Findings

Achieved 98.53% accuracy on simulated data

Extracted an average of 12 motor units per trial from experimental data

Matched 90.38% of units with expert-guided offline results

Abstract

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.