Best Practices in EEG Analysis: Preprocessing, Modeling, and Machine Learning

2026-09-28

AI Summary

This study addresses the analytical unreliability of electroencephalography (EEG) signals arising from artifact susceptibility, low signal-to-noise ratios, and substantial inter-subject variability. To overcome these challenges, we present a comprehensive modern EEG analysis pipeline encompassing preprocessing, statistical modeling, and machine learning. Methodologically, this work systematically integrates independent component analysis for artifact removal, time-frequency analysis, source localization, multivariate decoding, and deep learning techniques, bridging traditional signal processing with emerging EEG foundation models while strictly adhering to BIDS and FAIR data standards. The primary contribution lies in establishing best-practice guidelines for cross-subject generalization and equitable benchmarking. Ultimately, this framework provides the field with a reliable, interpretable, and highly reproducible reference architecture for end-to-end EEG analysis.

Abstract

Electroencephalography (EEG) analysis requires careful choices in preprocessing, statistical modeling, and machine learning because EEG signals are highly susceptible to artifacts, volume conduction, low signal-to-noise ratio, and substantial inter-subject variability. This chapter provides a practical and methodological guide to modern EEG analysis, spanning EEG preprocessing, artifact removal, filtering, bad-channel detection and interpolation, re-referencing, independent component analysis (ICA), and preprocessing of simultaneous EEG-fMRI recordings. We review major approaches for computational EEG analysis, including event-related potentials (ERPs), time-frequency analysis, functional and effective connectivity, source localization, multivariate decoding, permutation testing, and multiple-comparison correction. We then examine machine-learning methods for EEG, from feature-based classifiers to deep learning and emerging EEG foundation models, with emphasis on cross-subject generalization, limited-data regimes, data leakage, evaluation metrics, and fair benchmarking. Reproducibility is treated as a core requirement throughout, including transparent preprocessing, BIDS-EEG data organization, standardized derivatives, preservation of raw data, and FAIR data practices. The chapter is intended as a practical reference for researchers developing reliable, interpretable, and reproducible EEG analysis and machine-learning pipelines.

Problem

• EEG analysis
• preprocessing
• machine learning
• reproducibility
• artifact removal

Innovation

• EEG preprocessing
• Machine learning
• Foundation models
• Reproducibility
• Cross-subject generalization