Probing meaningfulness of oscillatory EEG components with bootstrapping, label noise and reduced training sets.

As oscillatory components of the Electroencephalogram (EEG) and other electrophysiological signals may co-modulate in power with a target variable of interest (e.g. reaction time), data-driven supervised methods have been developed to automatically identify such components based on labeled example trials. Under conditions of challenging signal-to-noise ratio, high-dimensional data and small training sets, however, these methods may overfit to meaningless solutions. Examples are spatial filtering methods like Common Spatial Patterns (CSP) and Source Power Comodulation (SPoC). It is difficult for the practitioner to tell apart meaningful from arbitrary, random components. We propose three approaches to probe the robustness of extracted oscillatory components and show their application to both, simulated and EEG data recorded during a visually cued hand motor reaction time task.