Maximally reliable spatial filtering of steady state visual evoked potentials

TL;DR

This paper introduces Reliable Components Analysis, a novel spatial filtering method that extracts high SNR, reproducible SSVEP components by maximizing trial-to-trial spectral covariance, outperforming traditional methods.

Contribution

The paper presents a new spatial filtering technique that enhances SSVEP analysis by focusing on trial-to-trial reliability, capturing most variance in fewer components, and providing a MATLAB implementation.

Findings

Recover physiologically plausible components matching underlying sources

Capture over 90% of trial-to-trial reliability in first four components

Achieve higher SNR than single electrodes or PCA

Abstract

Due to their high signal-to-noise ratio (SNR) and robustness to artifacts, steady state visual evoked potentials (SSVEPs) are a popular technique for studying neural processing in the human visual system. SSVEPs are conventionally analyzed at individual electrodes or linear combinations of electrodes which maximize some variant of the SNR. Here we exploit the fundamental assumption of evoked responses -- reproducibility across trials -- to develop a technique that extracts a small number of high SNR, maximally reliable SSVEP components. This novel spatial filtering method operates on an array of Fourier coefficients and projects the data into a low-dimensional space in which the trial-to-trial spectral covariance is maximized. When applied to two sample data sets, the resulting technique recovers physiologically plausible components (i.e., the recovered topographies match the lead fields of the underlying sources) while drastically reducing the dimensionality of the data (i.e., more than 90% of the trial-to-trial reliability is captured in the first four components). Moreover, the proposed technique achieves a higher SNR than that of the single-best electrode or the Principal Components. We provide a freely-available MATLAB implementation of the proposed technique, herein termed "Reliable Components Analysis".