Constrained Online Recursive Source Separation Framework for Real-time Electrophysiological Signal Processing

TL;DR

This paper introduces CORSS, a real-time blind source separation framework for electrophysiological signals that achieves high accuracy with minimal delay, suitable for neural-machine interfaces and clinical monitoring.

Contribution

The paper presents a novel constrained online recursive source separation method that incorporates prior information for improved real-time electrophysiological signal processing.

Findings

Achieved 96% matching rate in sEMG decomposition

Achieved 98.12% matching rate in respiratory signal extraction

Minimal 12.5 ms delay in processing

Abstract

Background and Objective: Processing electrophysiological signals often requires blind source separation (BSS) due to the nature of mixing source signals. However, its complex computational demands make real-time BSS challenging. The objective of this work is to develop an advanced real-time BSS method suitable for processing electrophysiological signals. Methods: In this paper, a novel BSS framework termed constrained online recursive source separation (CORSS) was proposed. In the framework, a stepwise recursive unmixing matrix learning rule was adopted to enable real-time updates with minimal computational cost. Moreover, by incorporating prior information of target signals to optimize the cost function, the framework algorithm was more likely to converge to the target sources. To validate its performance, the proposed framework was applied to both downstream tasks, namely real-time surface electromyogram (sEMG) decomposition and real-time respiratory intent monitoring based on diaphragmatic electromyogram (sEMGdi) extraction. Results: The proposed method achieved a matching rate of 96.00 % for the sEMG decomposition task and 98.12 % for the sEMGdi extraction task, exhibiting superior performance over other comparison methods (p < 0.05). Our method also exhibited minimal time delay during computation, with only 12.5 ms delay when the block size was 0.1s, demonstrating its good capabilities in online processing. Conclusions: The proposed method was demonstrated to enable real-time BSS with both improved separation performance and low computational latency. It is of substantial importance for real-time electrophysiological signal processing and applications towards advanced neural-machine interaction and clinical monitoring.