重审欧氏对齐方法，提升脑电接口迁移学习效率
Revisiting Euclidean Alignment for Transfer Learning in EEG-Based Brain-Computer Interfaces
- 通过欧氏对齐减少不同被试间脑电信号分布差异
- 13种脑机接口范式验证其有效且高效
- 适合关注脑电信号解码的科研人员参考
由于脑电（EEG）信号存在显著的被试内与被试间差异，基于EEG的脑机接口（BCIs）通常需要为每位新受试者进行个性化校准，耗时且不友好，限制了实际应用。迁移学习（TL）被广泛用于加速校准过程，利用其他被试或会话的数据。在针对EEG-BCI的迁移学习中，一个重要挑战是降低不同被试/会话间的数据分布差异，以避免负迁移。2020年提出的欧氏对齐（Euclidean alignment, EA）正是为此设计，已在13种不同脑机接口范式中多次实验验证其有效性与高效性。本文重新审视EA，阐明其流程与正确使用方式，介绍其应用场景与扩展，并指出潜在的新研究方向，对脑机接口研究者尤其是脑电信号解码方向的学者极具参考价值。
原文摘要 · Abstract (English)
Due to large intra-subject and inter-subject variabilities of electroencephalogram (EEG) signals, EEG-based brain-computer interfaces (BCIs) usually need subject-specific calibration to tailor the decoding algorithm for each new subject, which is time-consuming and user-unfriendly, hindering their real-world applications. Transfer learning (TL) has been extensively used to expedite the calibration, by making use of EEG data from other subjects/sessions. An important consideration in TL for EEG-based BCIs is to reduce the data distribution discrepancies among different subjects/sessions, to avoid negative transfer. Euclidean alignment (EA) was proposed in 2020 to address this challenge. Numerous experiments from 13 different BCI paradigms demonstrated its effectiveness and efficiency. This paper revisits EA, explaining its procedure and correct usage, introducing its applications and extensions, and pointing out potential new research directions. It should be very helpful to BCI researchers, especially those who are working on EEG signal decoding.
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