AI Insight
This systematic review examines variability in electroencephalography (EEG) measurements both between different individuals and within the same individual over time across multiple applications including resting-state recordings, event-related potentials, and brain-computer interfaces. The analysis reveals that inter-subject differences are generally larger than intra-subject fluctuations, with stability varying substantially by feature type: alpha-band measures show relatively good reliability while higher-frequency and connectivity metrics are more variable. The authors identify biological, state-related, technical, and analytical factors as major sources of variability and provide methodological recommendations for improving study design and data harmonization.
Why it matters
Understanding and accounting for EEG variability is critical for improving the reproducibility of neuroscience research, enhancing clinical diagnostic reliability, and developing more robust brain-computer interfaces that can generalize across individuals and sessions. The findings suggest that variability should not only be minimized through better methodology but also potentially leveraged as meaningful information for personalized neurotechnology applications.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Abstract: Electroencephalography (EEG) underpins neuroscience, clinical neurophysiology, and brain-computer interfaces (BCIs), yet pronounced inter- and intra-subject variability limits reliability, reproducibility, and translation. This systematic review studies that quantified or modeled EEG variability across resting-state, event-related potentials (ERPs), and task-related/BCI paradigms (including motor imagery and SSVEP) in healthy and clinical cohorts. Across paradigms, inter-subject differences are typically larger than within-subject fluctuations, but both affect inference and model generalization. Stability is feature-dependent: alpha-band measures and individual alpha peak frequency are often relatively reliable, whereas higher-frequency and many connectivity-derived metrics show more heterogeneous reliability; ERP reliability varies by component, with P300 measures frequently showing moderate-to-good stability. We summarize major sources of variability (biological, state-related, technical, and analytical), review common quantification and modeling approaches (e.g., ICC, CV, SNR, generalizability theory, and multivariate/learning-based methods), and provide recommendations for study design, reporting, and harmonization. Overall, EEG variability should be treated as both a practical constraint to manage and a meaningful signal to leverage for precision neuroscience and robust neurotechnology.
Source: Inter- and Intra-Subject Variability in EEG: A Systematic Survey