Lopez Jose D, Espinosa Jairo J, Barnes Gareth R
Mechatronics School, Universidad Nacional de Colombia sede Medellín, Medellín, Colombia.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:1534-7. doi: 10.1109/EMBC.2012.6346234.
MEG/EEG brain imaging has become an important tool in neuroimaging. Current techniques based in Bayesian approaches require an a-priori definition of patch locations on the cortical manifold. Too many patches results in a complex optimisation problem, too few an under sampling of the solution space. In this work random locations of the possible active regions of the brain are proposed to iteratively arrive at a solution. We use Bayesian model averaging to combine different possible solutions. The proposed methodology was tested with synthetic MEG datasets reducing the localisation error of the approaches based on fixed locations. Real data from a visual attention study was used for validation.
脑磁图/脑电图（MEG/EEG）脑成像已成为神经成像中的一项重要工具。当前基于贝叶斯方法的技术需要对皮质流形上的斑块位置进行先验定义。斑块过多会导致复杂的优化问题，斑块过少则会导致解空间的欠采样。在这项工作中，我们提出了大脑可能活跃区域的随机位置，以迭代方式得出解决方案。我们使用贝叶斯模型平均法来组合不同的可能解决方案。所提出的方法通过合成MEG数据集进行了测试，减少了基于固定位置的方法的定位误差。来自视觉注意力研究的真实数据用于验证。
