Time-frequency analysis: comparative exploration of methods in error monitoring

This study aims to compare various methods and measures of time-frequency analysis, with a focus on identifying both the similarities and differences between approaches. By conducting a systematic comparison, the study seeks to guide researchers in selecting the most appropriate methods for analyzing oscillatory activity. The time-frequency analysis techniques examined include wavelets, FFT, FOOOF, event-related spectral perturbation (ERSP), and time-frequency principal component analysis (PCA), all applied to a shared dataset. This dataset, derived from a previous study by Lin et al. (2020), includes data from 36 adults who performed a flanker task across two sessions. The study will evaluate the strengths and limitations of each technique, with key outcome measures including oscillatory power (delta, theta, and alpha bands) and phase-locking value. Additionally, test-retest reliability will be assessed. The findings will be interpreted in light of their theoretical implications for error-monitoring, specifically exploring the relationship between brain oscillations and event-related potentials (ERPs), such as the ERN and Pe. This research is a collaborative effort within the SPR working group on event-related brain oscillations (SPREO).