• Systematically evaluated the impact of downsampling on ERP analyses across seven common components (P3, N400, N170, N2pc, MMN, ERN, LRP). • The influence of univariate ERP measures depends on the measured ERP component and scoring methods • Multivariate decoding (MVPA) is robust to downsampling, maintaining high accuracy even at 64 Hz The choice of sampling rate is a critical preprocessing step in event-related potential (ERP) research, yet its impact on different analytic approaches remains underexplored. In this study, we systematically evaluated how downsampling affects data quality measured via Standardized Measurement Error (SME), conventional univariate ERP metrics (mean amplitude, peak amplitude, peak latency, and 50% area latency), and multivariate pattern analysis (MVPA; decoding). We analyzed seven commonly studied ERP components: P3, N400, N170, N2pc, mismatch negativity, error-related negativity, and lateralized readiness potential collected from neurotypical young adults. Across omnibus analyses, sampling rate did not produce significant global effects on data quality, conventional ERP metrics, or decoding performance within the tested range (64-1024 Hz). However, exploratory pairwise comparisons revealed selective, measure-specific differences at lower sampling rates. In particular, latency-based measures such as 50% area latency showed increased SME at 64 Hz, suggesting reduced temporal precision under coarse sampling. Effect sizes for most ERP measures remained stable at 128 Hz and above, with noticeable attenuation primarily at 64 Hz. In contrast, multivariate decoding performance was highly robust across sampling rates, with both classification accuracy and effect sizes remaining stable even at 64 Hz. Together, these findings indicate that sampling rate does not exert a systematic influence on ERP or decoding metrics within the commonly used range, although very low sampling rates may selectively affect latency-sensitive measures. For studies focusing on conventional ERP analyses, moderate-to-high sampling rates are advisable when precise temporal estimates are required. In contrast, lower sampling rates may be sufficient for decoding analyses when fine-grained temporal precision is not essential. For researchers analyzing ERP data with similar components, intra-individual variability levels, and participant populations as in this study, following these recommendations should yield robust statistical power.
Zhang et al. (Fri,) studied this question.