Laplacian reference is optimal for steady-state visual-evoked potentials

Abstract

Steady-state visual evoked potentials (SSVEPs) are widely used in human neuroscience studies and applications such as brain-computer interfaces. Surprisingly, no previous study has systematically evaluated different reference methods for SSVEP analysis, despite that signal reference is crucial for the proper assessment of neural activities. In the present study, using four datasets from our previous SSVEP studies (1-3) and three public datasets from other studies (4-6), we compared four reference methods: monopolar reference, common average reference, averaged-mastoids reference, and Laplacian reference. The quality of the resulting SSVEP signals was compared in terms of both signal-to-noise ratios (SNRs) and reliability. The results showed that Laplacian reference, which uses signals at the maximally activated electrode after subtracting the average of the nearby electrodes to reduce common noise, gave rise to the highest SNRs. Furthermore, the Laplacian reference resulted in SSVEP signals that were highly reliable across recording sessions or trials. These results suggest that Laplacian reference is optimal for SSVEP studies and applications. Laplacian reference is especially advantageous for SSVEP experiments where short preparation time is preferred, since it requires only data from the maximally activated electrode and a few surrounding electrodes.

Feedback negativity and feedback‐related P3 in individuals at risk for depression: Comparing surface potentials and current source densities

“…This study aimed to address this shortcoming. To be sure, we did not intend to again reevaluate the known benefits of CSD over ERP methods which are generic and not specific to any particular component (e.g., see Kayser & Tenke, 2015a, 2015b, 2015cZhang et al, 2023). Rather, we aimed to provide a side-by-side comparison of findings stemming from reference-dependent versus reference-free data transformations to inform the field in an area of research that has yet to adopt referencefree techniques more broadly .…”

“…A CSD transform, also referred to as the scalp surface Laplacian, provides estimates of radial current flow at scalp (sinks and sources) that have sharper topographies and improved temporal resolution compared to ERP signals smeared by volume conduction (Burle et al, 2015;Nunez & Srinivasan, 2006;. Although direct systematic comparisons have demonstrated superior statistical properties of CSD over ERP signals (e.g., Kayser & Tenke, 2006a, 2006b, 2015aZhang et al, 2023), in addition to their theoretical and interpretational advantages, to the best of our knowledge, no such comparison has been performed for ERP components related to reward feedback, even though a few studies have employed CSD methods (Kamarajan et al, 2015;Nelson & Jarcho, 2021). This study aimed to address this shortcoming.…”