一种用于从具有大量背景脑活动的脑磁图数据中估计刺激诱发脑反应的图形模型。
A graphical model for estimating stimulus-evoked brain responses from magnetoencephalography data with large background brain activity.
作者信息
Nagarajan Srikantan S, Attias Hagai T, Hild Kenneth E, Sekihara Kensuke
机构信息
Biomagnetic Imaging Laboratory, Department of Radiology, University of California at San Francisco, San Francisco, CA 94122, USA.
出版信息
Neuroimage. 2006 Apr 1;30(2):400-16. doi: 10.1016/j.neuroimage.2005.09.055. Epub 2005 Dec 19.
Abstract
This paper formulates a novel probabilistic graphical model for noisy stimulus-evoked MEG and EEG sensor data obtained in the presence of large background brain activity. The model describes the observed data in terms of unobserved evoked and background factors with additive sensor noise. We present an expectation maximization (EM) algorithm that estimates the model parameters from data. Using the model, the algorithm cleans the stimulus-evoked data by removing interference from background factors and noise artifacts and separates those data into contributions from independent factors. We demonstrate on real and simulated data that the algorithm outperforms benchmark methods for denoising and separation. We also show that the algorithm improves the performance of localization with beamforming algorithms.
摘要
本文针对在存在大量背景脑电活动的情况下获取的有噪声刺激诱发的脑磁图（MEG）和脑电图（EEG）传感器数据，制定了一种新颖的概率图形模型。该模型根据具有加性传感器噪声的未观察到的诱发因素和背景因素来描述观测数据。我们提出了一种期望最大化（EM）算法，该算法可从数据中估计模型参数。使用该模型，该算法通过消除背景因素和噪声伪影的干扰来清理刺激诱发的数据，并将这些数据分离为独立因素的贡献。我们在真实数据和模拟数据上证明，该算法在去噪和分离方面优于基准方法。我们还表明，该算法提高了波束形成算法的定位性能。
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