脑电图分析的最佳实践：预处理、建模与机器学习
Best Practices in EEG Analysis: Preprocessing, Modeling, and Machine Learning
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中文总结 AI 辅助
本文提供EEG分析全流程的实用指南，涵盖预处理、建模与机器学习方法，强调可重复性、跨受试者泛化及公平基准测试，以支持可靠可解释的EEG分析流程。
中文摘要 AI 辅助
脑电图（EEG）分析需要在预处理、统计建模和机器学习方面做出谨慎选择，因为EEG信号极易受到伪迹、容积传导、低信噪比以及显著的受试者间变异性的影响。本章为现代EEG分析提供了一份实用且方法论的指南，涵盖EEG预处理、伪迹去除、滤波、坏通道检测与插值、重新参考、独立成分分析（ICA）以及同步EEG-fMRI记录的预处理。我们回顾了计算性EEG分析的主要方法，包括事件相关电位（ERPs）、时频分析、功能性和有效连接性、源定位、多变量解码、置换检验以及多重比较校正。随后，我们考察了用于EEG的机器学习方法，从基于特征的分类器到深度学习以及新兴的EEG基础模型，重点关注跨受试者泛化、有限数据场景、数据泄漏、评估指标和公平基准测试。可重复性在整个过程中被视为核心要求，包括透明的预处理、BIDS-EEG数据组织、标准化的衍生数据、原始数据的保留以及FAIR数据实践。本章旨在为研究人员开发可靠、可解释且可重复的EEG分析和机器学习流程提供实用参考。
英文摘要
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.
发表机构
- University of Southern California（南加州大学）
- McGill University（麦吉尔大学）
机构由 AI 辅助整理，请以论文原文为准。
