Single-Trial Normalization for Event-Related Spectral Decomposition Reduces Sensitivity to Noisy Trials

In electroencephalography, the classical event-related potential model often proves to be a limited method to study complex brain dynamics. For this reason, spectral techniques adapted from signal processing such as event-related spectral perturbation (ERSP) – and its variant event-related synchronization and event-related desynchronization – have been used over the past 20 years. They represent average spectral changes in response to a stimulus. These spectral methods do not have strong consensus for comparing pre- and post-stimulus activity. When computing ERSP, pre-stimulus baseline removal is usually performed after averaging the spectral estimate of multiple trials. Correcting the baseline of each single-trial prior to averaging spectral estimates is an alternative baseline correction method. However, we show that this method leads to positively skewed post-stimulus ERSP values. We eventually present new single-trial-based ERSP baseline correction methods that perform trial normalization or centering prior to applying classical baseline correction methods. We show that single-trial correction methods minimize the contribution of artifactual data trials with high-amplitude spectral estimates and are robust to outliers when performing statistical inference testing. We then characterize these methods in terms of their time–frequency responses and behavior compared to classical ERSP methods.