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.