Guidance for reliable and reproducible EEG signal analysis
Best Practices in EEG Analysis: Preprocessing, Modeling, and Machine Learning
Machine Learning
Summary
EEG signals, which record brain activity, are tricky to analyze because they contain a lot of noise and vary between people. The authors provide a detailed guide on how to clean, process, and analyze EEG data using statistical and machine learning tools. They emphasize methods for removing artifacts, organizing data, and ensuring results can be replicated. Their work helps researchers build reliable and interpretable EEG studies.
What this means in practice
- •For clinical neurophysiology teams: Follow standardized EEG preprocessing and artifact correction protocols to improve reliability in diagnosis and brain monitoring.
- •For brain-computer interface developers: Use reviewed machine learning practices for better generalization and reproducibility when decoding EEG signals across different users.
A survey. It maps existing work.
Authors
Parsa Razmara, Woojae Jeong, Aditya Kommineni, Raymundo Cassani, Richard Leahy, Takfarinas Medani
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.