In this paper, we proposed a multi-objective approach for the EEG Inverse Problem. This formulation does not need unknown parameters that involve empirical procedures. Due to the combinatorial characteristics of the problem, this alternative included evolutionary strategies to resolve it. The result is a Multi-objective Evolutionary Algorithm based on Anatomical Restrictions (MOEAAR) to estimate distributed solutions. The comparative tests were between this approach and 3 classic methods of regularization: LASSO, Ridge-L and ENET-L. In the experimental phase, regression models were selected to obtain sparse and distributed solutions. The analysis involved simulated data with different signal-to-noise ratio (SNR). The indicators for quality control were Localization Error, Spatial Resolution and Visibility. The MOEAAR evidenced better stability than the classic methods in the reconstruction and localization of the maximum activation. The norm L0 was used to estimate sparse solutions with the evolutionary approach and its results were relevant.

中文翻译：

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脑电逆问题的多目标进化算法

在本文中，我们提出了一种针对 EEG 逆问题的多目标方法。该公式不需要涉及经验程序的未知参数。由于问题的组合特征，该替代方案包括解决该问题的进化策略。结果是基于解剖限制 (MOEAAR) 的多目标进化算法来估计分布式解决方案。比较测试是在这种方法和 3 种经典的正则化方法之间进行的：LASSO、Ridge-L 和 ENET-L。在实验阶段，选择回归模型来获得稀疏和分布的解决方案。分析涉及具有不同信噪比 (SNR) 的模拟数据。质量控制的指标是定位误差、空间分辨率和可见度。MOEAAR 在最大激活的重建和定位方面证明了比经典方法更好的稳定性。范数 L0 用于通过进化方法估计稀疏解，其结果是相关的。