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.
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Publication details
- Journal
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- Open access
- Green open access
Cite this article
APA 7
Razmara, P., Jeong, W., Kommineni, A., Cassani, R., Leahy, R., & Medani, T. (2026). Best Practices in EEG Analysis: Preprocessing, Modeling, and Machine Learning. https://omanscience.com/en/articles/best-practices-in-eeg-analysis-preprocessing-modeling-and-machine-learning
MLA 9
Razmara, Parsa, et al. "Best Practices in EEG Analysis: Preprocessing, Modeling, and Machine Learning." https://omanscience.com/en/articles/best-practices-in-eeg-analysis-preprocessing-modeling-and-machine-learning.
Chicago (author–date)
Razmara, Parsa, Woojae Jeong, Aditya Kommineni, Raymundo Cassani, Richard Leahy, and Takfarinas Medani. 2026. "Best Practices in EEG Analysis: Preprocessing, Modeling, and Machine Learning." https://omanscience.com/en/articles/best-practices-in-eeg-analysis-preprocessing-modeling-and-machine-learning.
Harvard
Razmara, P., Jeong, W., Kommineni, A., Cassani, R., Leahy, R. and Medani, T. (2026) 'Best Practices in EEG Analysis: Preprocessing, Modeling, and Machine Learning', Available at: https://omanscience.com/en/articles/best-practices-in-eeg-analysis-preprocessing-modeling-and-machine-learning.
Vancouver
Razmara P, Jeong W, Kommineni A, Cassani R, Leahy R, Medani T. Best Practices in EEG Analysis: Preprocessing, Modeling, and Machine Learning. https://omanscience.com/en/articles/best-practices-in-eeg-analysis-preprocessing-modeling-and-machine-learning
IEEE
P. Razmara, W. Jeong, A. Kommineni, R. Cassani, R. Leahy, and T. Medani, "Best Practices in EEG Analysis: Preprocessing, Modeling, and Machine Learning," https://omanscience.com/en/articles/best-practices-in-eeg-analysis-preprocessing-modeling-and-machine-learning.