SPDAlign：用于脑电正向建模偏移的可解释黎曼对齐

中文摘要 AI 辅助

基于脑电图（EEG）的脑机接口能够实现大脑与设备之间的直接通信，应用于康复和通信等领域。然而，由于EEG数据的非平稳特性会引入跨域（例如会话和受试者）的分布偏移，其实际效用常常受到限制。以无监督方式使机器学习模型对这些偏移具有不变性，而无需使用昂贵的标记校准数据，将极大提升EEG数据的实用性。在本工作中，我们使用经典的EEG生成模型来研究由域特定正向过程（与头部几何等因素相关）引入的分布偏移。我们从理论上证明，此类分布偏移仅通过对称正定流形上的线性变换即可恢复。基于这一见解，我们提出了SPDAlign，一个用于促进域不变EEG学习的可解释框架。SPDAlign首先对齐域特定均值，并使用一种称为Wasserstein Procrustes的最新最优传输技术来校正跨域的全局旋转。我们通过模拟系统性地研究了所提出的方法，并在广泛的公共EEG数据集上展示了其具有竞争力的性能。此外，SPDAlign是一个全局线性框架，且本质上可解释，因此该框架能够识别感兴趣的频率范围，确定反映源-传感器关系的空间模式，并解决跨受试者变异性问题。

英文摘要

Electroencephalography (EEG) based brain-computer interfaces enable direct brain-to-device communication for applications such as rehabilitation and communication. However, their practical utility is often limited as the non-stationary nature of the EEG data introduces distribution shifts across domains (e.g., sessions and subjects). Adapting machine learning models to be invariant to these shifts in an unsupervised way, without using costly labeled calibration data, would drastically improve the utility of EEG data. In this work, we use a classic generative model of EEG to study distribution shifts introduced by the domain-specific forward process, which is associated with factors such as head geometry. We theoretically show that such distribution shifts can be recovered solely through linear transformations on the Symmetric Positive Definite manifold. Building on this insight, we propose SPDAlign, an interpretable framework for promoting domain-invariant EEG learning. SPDAlign first aligns the domain-specific means and corrects global rotations across domains using a recent optimal transport technique called Wasserstein Procrustes. We systematically study the proposed approach through simulations and demonstrate its competitive performance on extensive public EEG datasets. Additionally, SPDAlign is a globally linear framework and is intrinsically interpretable, so that the framework can identify frequency ranges of interest, determine the spatial patterns reflecting source-sensor relationships, and address cross-subject variability.

发表机构

• BIFOLD（柏林智能数据与机器学习研究所）
• Technical University of Berlin（柏林工业大学）
• Advanced Telecommunications Research Institute International（国际电气通信基础技术研究所）
• RIKEN AIP（理化学研究所先进智能研究中心）
• Nanyang Technological University（南洋理工大学）

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