Dual-sided EEG electrode signals have varying correlations that depend on movement characteristics.

Mobile Electroencephalography (EEG) measures human brain activity during locomotion, extending brain dynamics research into real-world scenarios. However, EEG is highly susceptible to artifacts, and more reliable approaches are needed to attenuate motion artifacts in mobile EEG recordings. Dual-layer EEG, which records scalp EEG simultaneously with isolated motion artifact signals, presents a novel and promising approach. This method assumes that noise-biased EEG data captures the common noise as the isolated motion artifact data. The purpose of this study was to investigate the relationship between signals from both sides of the electrode and their relationship to movement. We developed a benchtop test platform where the top and bottom sides of a dual-sided electrode interfaced with conductive fabric. Using a robotic arm, we moved the dual-sided electrode with different directions, magnitudes, frequencies, and randomness. We quantified correlations between signals from the top and bottom sides to understand the relationship of the dual-sided signals. We also quantified correlations between movement and signals from both the top and bottom sides to examine the extent to which the signals are related to movement. Movement direction affected the correlation signs, and all correlation metrics scaled with movement magnitude rather than frequency. Increased movement randomness reduced top and bottom signal correlation. These findings contribute new insights into signal correlations of the dual-sided EEG electrode and could inform the development of improved dual-layer EEG algorithms and hardware innovations for real-world applications.