aiqbench analysis
This chapter serves as a critical methodological reference for advancing machine learning applications in neurotechnology by addressing the unique challenges of EEG data, such as artifacts and inter-subject variability. It specifically highlights emerging deep learning approaches and foundation models while emphasizing rigorous reproducibility standards like BIDS-EEG to ensure robust cross-subject generalization.
Best Practices in EEG Analysis: Preprocessing, Modeling, and Machine Learning
arXiv:2609.36609v1 Announce Type: cross 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.
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.
38 pages, 5 figures. Chapter 16 of the 2026 monograph "Electroencephalogram Signals and Cognition," edited by Hema A. Murthy, Shrikanth Narayanan, Mriganka Sur, and Rajeswari Aghoram
Signal Processing (eess.SP); Machine Learning (cs.LG)
arXiv:2609.36609 [eess.SP]
(or arXiv:2609.36609v1 [eess.SP] for this version)
https://doi.org/10.48550/arXiv.2609.36609 arXiv-issued DOI via DataCite (pending registration)
In: Electroencephalogram Signals and Cognition, Hema A. Murthy, Shrikanth Narayanan, Mriganka Sur, and Rajeswari Aghoram (eds.), Sriranga Digital Software Technologies Pvt. Ltd., 2026, Chapter 16, pp. 327-370
Full text under the publisher's open licence · 240 words
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