由头皮上的电压推断出大脑内神经活动源的过程称之为脑电逆问题，这一问题的解决具有重要的研究意义和应用价值。为了有效地进行脑电逆问题的反演计算，提出了一种基于状态空间的新的脑电逆问题求解算法。该方法首先根据神经系统的动力学方程得到状态方程，并由脑电系统的观测方程构成测量方程；然后应用卡尔曼滤波方法来反演大脑内活动源的信息。这种新的求逆算法不仅可以处理脑电系统中的不确定因素，而且还可以将静态和动态脑电逆问题的求解统一到同一框架下，因此具有一定的新颖性；最后分别给出了模拟数据和实际脑电数据的实验结果。实验结果证明，卡尔曼滤波法更具优越性。

Estimating the information of electric activity source within the brain from the potential distribution measured on the scalp is called EEG (electroencephalographic) inverse problem. In this paper，a new method based on state space model is proposed. The proposed strategy formulates the source activity distribution through kinetics of brain neurons，and the potential distribution measured on the scalp through observation equations，thus makes it possible to unify the dynamic reconstruction problem and static reconstruction problem into a general framework. Further，it coherently treats the uncertainties of the statistical model of the imaging system and the noisy nature of measurement data. The performance of the proposed framework is evaluated using simulated phantom data and real EEG data with favorable results.