求解脑电逆问题的非线性局域优化方法探讨

讨论了基于阻尼最小二乘算法、单纯形算法等非线性局域优化方法的脑电偶极子定位问题.在已知源数目的条件下,分析了源定位误差与噪声水平的关系.在源数目未能确定时,讨论了反演模型中源数目与实际源数目不一致的情况,并给出了相应的判别准则.分析了脑电模拟观测电位值与等效偶极子产生电位值的拟合程度.另外,比较了上述两种优化方法的运行速度及计算精度.仿真结果表明,在处理一个或两个源场的情况下,如果迭代初值选择合理,非线性局域优化方法可以有效地解决脑电逆问题.

Discussion of nonlinear local optimization methods for EEG inverse problem solution

EEG inverse problem solution is an important issue in EEG research. Nonlinear local optimization methods, such as Simplex and Levenberg-Marquart algorithms, were used to solve EEG dipole source localization problems. The relationship between localizing errors and noise level was discussed on the condition that the source number was known. If the source number was unknown, the selected number in model might not equal to the actual one, then a computation was carried out and a corresponding discrimination criteria was proposed. Approximation accuracy between the simulation measurements and dipole-generated potentials was given. The computation speed and the localizing results between the two algorithms were compared. Computer simulation demonstrates that nonlinear local optimization methods are effective for EEG inverse solution if the source number is one or two and the initial iterative values are reasonable.