Multivariate Autoregressive Independent Component Analysis for Real-Time Resting-State Functional Connectivity in High-Density EEG

In this study, we developed and evaluated a real-time implementation of the multivariate auto-regressive independent component analysis (MVARICA) model for estimating effective connectivity using Partial Directed Coherence (PDC). Each step of the MVARICA pipeline was adapted for online processing, with a focus on optimizing key hyperparameters, specifically model order and the delta ridge penalty in real time. The performance of the online model was benchmarked against a gold-standard offline MVARICA implementation. Our real-time model achieved a Mean Absolute Error (MAE) of 0.070 (7% error), with 95% of the values falling within a 20% deviation from the offline reference. Error varied across frequency bands: Delta (MAE = 11.5%), Theta (9.1%), Alpha (7.8%), Beta (5.5%), and Gamma (3.6%). The Pearson correlation across all frequency bands exceeded 0.744, indicating strong agreement between the online and offline models. The real-time model successfully captured the key connectivity patterns identified by the offline version, converged reliably on hyperparameter optimization, and operated within real-time constraints for a low number of channels. Specifically, latency remained under 100 ms for single-channel inputs, but increased to approximately 1 second for high-density configurations (e.g., 109 channels).Keywords: Electroencephalography, Resting-state Functional Connectivity, MVARICA, Convolutional Neural Network, Deep Learning.