基于二阶统计量近似联合对角化的人脑电图盲源分离
On the blind source separation of human electroencephalogram by approximate joint diagonalization of second order statistics
AI总结：
本文综述了基于二阶统计量（SOS）和高阶统计量（HOS）的盲源分离（BSS）在人脑电图（EEG）分析中的应用，提出了一种基于傅里叶共谱矩阵近似联合对角化（AJDC）的时频域BSS方案，并讨论了其假设与局限性。
摘要：
在过去十年中，盲源分离（BSS）已成为人类脑电图（EEG）研究中的重要处理工具。无需依赖头部模型，BSS旨在估计引起观测EEG的颅内偶极电流的波形和头皮空间模式。在这篇综述中，我们首先将BSS线性瞬时模型置于脑容积传导理论的框架内。然后，我们回顾了基于二阶统计量（SOS）和基于高阶统计量（HOS）的BSS的概念和当前实践，后者更广为人知的是独立成分分析（ICA）。利用神经生理学知识，我们考虑了基于SOS和基于HOS的方法在提取自发和诱发EEG以及将其与颅外伪迹分离方面的适用性。随后，我们展示了一个仅在时频域中使用SOS的通用BSS方案。该方案易于扩展到进一步的数据扩展，以捕获实验变异源。描述了一种基于傅里叶共谱矩阵近似联合对角化（AJDC）的简单高效实现。最后，我们讨论了EEG的BSS分析的有用方面，包括其假设和局限性。
英文摘要：
Over the last ten years blind source separation (BSS) has become a prominent processing tool in the study of human electroencephalography (EEG). Without relying on head modeling BSS aims at estimating both the waveform and the scalp spatial pattern of the intracranial dipolar current responsible of the observed EEG. In this review we begin by placing the BSS linear instantaneous model of EEG within the framework of brain volume conduction theory. We then review the concept and current practice of BSS based on second-order statistics (SOS) and on higher-order statistics (HOS), the latter better known as independent component analysis (ICA). Using neurophysiological knowledge we consider the fitness of SOS-based and HOS-based methods for the extraction of spontaneous and induced EEG and their separation from extra-cranial artifacts. We then illustrate a general BSS scheme operating in the time-frequency domain using SOS only. The scheme readily extends to further data expansions in order to capture experimental source of variations as well. A simple and efficient implementation based on the approximate joint diagonalization of Fourier cospectral matrices is described (AJDC). We conclude discussing useful aspects of BSS analysis of EEG, including its assumptions and limitations.
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