Single-trial normalization for event-related spectral decomposition reduces sensitivity to noisy trials

Abstract

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In EEG research, the classical Event-Related Potential (ERP) model often proves to be a limited method when studying complex brain dynamics. For this reason, spectral techniques adapted from signal processing such as Event-Related Spectral Perturbation (ERSP) – and its variant ERS (Event-Related Synchronization) and ERD (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 when performing statistical inference testing compared to classical ERSP methods.

Keywords

• EEG
• ERP
• additive model
• baseline
• ERSP
• multiplicative gain model