Department of Complexity Science and Engineering, The University of Tokyo, Tokyo, Japan.
Med Biol Eng Comput. 2013 Aug;51(8):937-51. doi: 10.1007/s11517-013-1069-y. Epub 2013 May 9.
We presented a method of rejecting sensor-specific and environmental noise during magnetoencephalography (MEG) measurement that enables the extraction of brain signals from single-epoch data. The method assumes a parametric generative model of MEG data. The model's optimal parameters were determined from single-epoch data, and noise reduction was performed by the decomposition of data within the optimal model. We confirmed our method's validity through multiple experiments. Moreover, we compared our method's performance with that of several previous noise-reduction methods. Finally, we confirmed that the proposed method followed by spatial filtering reduced noise more efficiently.
我们提出了一种在脑磁图（MEG）测量中拒绝传感器特定和环境噪声的方法，该方法可从单epoch 数据中提取脑信号。该方法假设 MEG 数据的参数生成模型。从单 epoch 数据中确定模型的最佳参数，并通过在最佳模型内分解数据来进行降噪。我们通过多个实验验证了我们方法的有效性。此外，我们还比较了我们的方法与几种先前的降噪方法的性能。最后，我们确认经过空间滤波的提出方法能够更有效地降低噪声。
