Evaluation of reference phase analysis for synchronous EEG source separation

Recent research has demonstrated that synchrony is fundamental for efficient brain function. Specifically, phase synchrony plays a pivotal role in corticomuscular control. Grasping these phase relationships is vital for understanding brain motor control mechanisms and addressing motor-related disorders. Electrophysiological recordings of brain activity typically involve a superposition of source signals, resulting in misleading synchronization measurements. Hence, synchronous source separation techniques become crucial for studying brain phase synchronization. This paper introduces the Reference Phase Analysis (RPA), an algorithm designed to identify brain sources phase-locked to a given reference. We assessed the algorithm’s performance using simulated and real electrophysiological data. Various factors, including the number of neuronal sources and different levels/types of noise, were tested. The RPA algorithm accurately identified synchronous sources in simulated electrophysiological data, maintaining robust performance under substantial noise. Estimated synchronous sources showed high correlation and PLV ( > 0.99) up to 60 dB of additive noise, and PLV greater than 0.90 for phase jitters under 0.8 radians. Even with six mixed sources at a 20 dB SNR, the synchronous source remained detectable, with a correlation of 0.97, compared to the simulated one. Preliminary results from real recordings further support the method’s effectiveness for investigating corticomuscular control. The proposed algorithm addresses the lack of techniques for separating synchronous sources and overcomes key methodological limitations of traditional methods. Using RPA enables precise estimation of synchrony between electrophysiological sources, enhancing understanding of neuronal integration and control mechanisms.