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首页> 外文期刊>NeuroImage >A probabilistic algorithm integrating source localization and noise suppression for MEG and EEG data.
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A probabilistic algorithm integrating source localization and noise suppression for MEG and EEG data.

机译：一种集成了MEG和EEG数据源定位和噪声抑制的概率算法。

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摘要

We have developed a novel probabilistic model that estimates neural source activity measured by MEG and EEG data while suppressing the effect of interference and noise sources. The model estimates contributions to sensor data from evoked sources, interference sources and sensor noise using Bayesian methods and by exploiting knowledge about their timing and spatial covariance properties. Full posterior distributions are computed rather than just the MAP estimates. In simulation, the algorithm can accurately localize and estimate the time courses of several simultaneously active dipoles, with rotating or fixed orientation, at noise levels typical for averaged MEG data. The algorithm even performs reasonably at noise levels typical of an average of just a few trials. The algorithm is superior to beamforming techniques, which we show to be an approximation to our graphical model, in estimation of temporally correlated sources. Success of this algorithm using MEG data for localizing bilateral auditory cortex, low-SNR somatosensory activations, and for localizing an epileptic spike source are also demonstrated.
机译：我们已经开发了一种新颖的概率模型，该模型可估计通过MEG和EEG数据测得的神经源活动，同时抑制干扰源和噪声源的影响。该模型使用贝叶斯方法并利用有关它们的时间和空间协方差特性的知识来估计来自诱发源，干扰源和传感器噪声的传感器数据贡献。将计算全部后验分布，而不仅仅是MAP估计。在仿真中，该算法可以在平均MEG数据通常具有的噪声水平下，精确定位和估计几个同时旋转或固定取向的同时活动偶极子的时程。该算法甚至在只有几次试验的平均水平的噪声水平下仍能合理执行。该算法优于波束成形技术，在时间相关源的估计中，波束成形技术是图形模型的近似值。还证明了该算法使用MEG数据成功定位双侧听觉皮层，低SNR体感激活以及定位癫痫尖峰源的成功。

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