Zou Ling, Zhu Shan'an, He Bin
Department of Computer Science and Technology, Jiangsu Polytechnic University, Changzhou 213016, China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2006 Dec;23(6):1206-12.
We have investigated spatio-temporal source modeling (STSM) of the electroencephalogram (EEG) by using a Quasi-Newton method based on Independent Component Analysis (ICA) for localization of multiple dipole sources from the scalp EEG. The problem of multiple dipole localization was transformed into several single dipole localization problems. Another benefit of the present method is that the number of independent sources can be estimated. Computer simulation studies were conducted to evaluate the performance of this approach. The present simulation results indicate that the ICA-based method is superior to the conventional nonlinear methods in localization accuracy, computation time and anti-noise performance, for multiple dipole localization when the sources are stationary over the period of interest.
我们通过使用基于独立成分分析(ICA)的拟牛顿法对脑电图(EEG)进行时空源建模(STSM),以从头皮脑电图中定位多个偶极子源。多个偶极子定位问题被转化为几个单偶极子定位问题。本方法的另一个优点是可以估计独立源的数量。进行了计算机模拟研究以评估该方法的性能。目前的模拟结果表明,当源在感兴趣的时间段内静止时,基于ICA的方法在定位精度、计算时间和抗噪声性能方面优于传统的非线性方法,用于多个偶极子定位。