Evaluating Recurrent Neural Network Blind Source Separation of Event-Related Potentials Using Simulated Data

Abstract

Event-related potential (ERP) waveforms reflect spatiotemporal summation of potential differences resulting from the activity of current sources within the brain. Separating ERPs into underlying source waveforms and their scalp distributions is a potentially valuable for analyzing neurophysiology associated with psychophysiological events. However, ground-truth sources are unknown for real ERP data. This study aimed to evaluate recurrent neural network blind source separation (RNN-BSS) of ERP waveforms using simulated ERP data with known groundtruth source waveforms and scalp distributions. Simulated ERP waveforms were generated from seven simulated source waveforms and scalp distributions. Two simulations with positiveand negative-going source waveforms were evaluated to explore the effect of RNN source signal rectification on source representations. The source waveforms and scalp distributions extracted from applying RNN-BSS to simulated data were compared with ground-truth using Pearson's correlation coefficient. Source waveforms and scalp distributions extracted by RNN-BSS were highly correlated with their ground-truth counterparts. However, where scalp distributions of ground-truth sources were highly correlated, they were not separated perfectly by RNN-BSS. Negative-going simulations produced inverted scalp distributions. Overall, these results demonstrate efficacy of RNNBSS applied to simulated ERP waveforms with seven sources. Further evaluations are required to determine the limits of source separation when scalp distributions are highly correlated, and the influence of number of electrodes in RNN-BSS.