摘要翻译：
我们考虑了利用空间滤波（波束形成）从脑电图(EEG)或脑磁图(MEG)重建脑活动的问题。提出了基于最小方差伪无偏降秩估计（MV-PURE）框架的空间滤波器。它们有两种类型，这取决于EEG/MEG正向模型是否明确考虑“干扰活动”，即脑电活动源于感兴趣区域以外的大脑区域，在EEG/MEG传感器上记录为与感兴趣活动相关的信号。在这两种情况下，所提出的滤波器都配备了一个秩选择准则，使滤波器输出的均方误差(MSE)最小。因此，我们认为它们是著名的线性约束最小方差(LCMV)和零陷滤波器的新的非平凡推广。所提出的滤波器具有同样广泛的应用领域，其中特别包括基于重建的感兴趣源活动的有向连通性度量的评估，本文将其作为一个示例应用。此外，为了便于我们研究的可重复性，我们提供了（与本文联合）综合仿真框架，允许对应用于MEG或EEG信号的多个空间滤波器的信号重构误差进行估计。基于此框架，在一组详细的仿真中验证了所提滤波器的主要性能。
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英文标题：
《Reconstruction of Brain Activity from EEG/MEG Using MV-PURE Framework》
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作者：
Tomasz Piotrowski, Jan Nikadon, David Gutierrez
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最新提交年份：
2017
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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一级分类：Mathematics        数学
二级分类：Optimization and Control        优化与控制
分类描述：Operations research, linear programming, control theory, systems theory, optimal control, game theory
运筹学，线性规划，控制论，系统论，最优控制，博弈论
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英文摘要：
  We consider the problem of reconstruction of brain activity from electroencephalography (EEG) or magnetoencephalography (MEG) using spatial filtering (beamforming). We propose spatial filters which are based on the minimum-variance pseudo-unbiased reduced-rank estimation (MV-PURE) framework. They come in two flavours, depending whether the EEG/MEG forward model considers explicitly "interfering activity", understood as brain's electrical activity originating from brain areas other than regions of interest which is recorded at EEG/MEG sensors as a signal correlated with activity of interest. In both cases, the proposed filters are equipped with a rank-selection criterion minimizing the mean-square-error (MSE) of the filter output. Therefore, we consider them as novel nontrivial generalizations of well-known linearly constrained minimum-variance (LCMV) and nulling filters. The proposed filters have equally wide area of applications, which include in particular evaluation of directed connectivity measures based on the reconstructed activity of sources of interest, considered in this paper as a sample application. Moreover, in order to facilitate reproducibility of our research, we provide (jointly with this paper) comprehensive simulation framework that allows for estimation of error of signal reconstruction for a number of spatial filters applied to MEG or EEG signals. Based on this framework, chief properties of proposed filters are verified in a set of detailed simulations.
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PDF链接：
https://arxiv.org/pdf/1712.02997