Epoched

Epoched refers to the state or process of dividing continuous data into discrete segments, called epochs, typically centered around events or time points. Each epoch represents a short window of data, enabling analysis of responses that are tied to specific stimuli, trials, or moments in time.

In neuroscience and psychophysiology, epoched data is standard in EEG and MEG analysis, where recordings are segmented into epochs anchored to stimulus onset or participant response. More generally, time-series analysis uses epoched data by slicing data into fixed-length windows for windowed analysis, feature extraction, or event-related testing.

The typical workflow for epoched data includes selecting an epoch window, identifying event markers, and extracting segments from the continuous signal. Preprocessing steps such as filtering, downsampling, and artifact rejection are applied to each epoch. Analyses are then performed on the collection of epochs, for example by averaging across trials to obtain event-related potentials or by fitting models to epoch-level data.

Key considerations include the choice of epoch length, the baseline period used for correction, and padding strategies to mitigate edge effects. Epoching inherently trades some long-range context for improved alignment and comparability across trials, which can enhance signal-to-noise ratios in time-locked analyses.

In addition to neuroscience, epoched data concepts appear in other fields that rely on time references or event-centered analyses, where data are organized according to defined epochs to facilitate interpretation and comparison.