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

Steady-state visual-evoked potentials (SSVEPs) are widely used in human neuroscience studies and applications such as brain-computer interfaces (BCIs). 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 (Chen J, Valsecchi M, Gegenfurtner KR. 118: 749-754, 2017; Chen J, Valsecchi M, Gegenfurtner KR. 102: 206-216, 2017; Chen J, McManus M, Valsecchi M, Harris LR, Gegenfurtner KR. 19: 8, 2019) and three public datasets from other studies (Baker DH, Vilidaite G, Wade AR. 17: e1009507, 2021; Lygo FA, Richard B, Wade AR, Morland AB, Baker DH. 230: 117780, 2021; Vilidaite G, Norcia AM, West RJH, Elliott CJH, Pei F, Wade AR, Baker DH. 285: 20182255, 2018), 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 as it requires only data from the maximally activated electrode and a few surrounding electrodes. The present study provides a comprehensive evaluation of the use of different reference methods for steady-state visual-evoked potentials (SSVEPs) and has found that Laplacian reference increases signal-to-noise ratios (SNRs) and enhances reliabilities of SSVEP signals. Thus, the results suggest that Laplacian reference is optimal for SSVEP analysis.