Zhou Ting, Pan Lin, Yu Lun
College of Information Engineering, Fuzhou University, Fuzhou 350002, China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2009 Oct;26(5):967-71.
In this paper is proposed the use of proper orthogonal decomposition (POD) for the decomposition and reconstruction of electroencephalograph (EEG) signal. The EEG signal can be denoted as the composition of eigenfunction modes and main coordinate components. The eigenfunction mode is a deterministic function of spatial variables, which represents the spatial information of electrodes. And the main coordinate component is a random function of time variables, which represents the real time signal of EEG. So a new way is pointed out for eigenvector space compression and prediction of electro potential values for EEG signal in any random scalp position. Orientation of further studies is also discussed.
本文提出了使用本征正交分解（POD）对脑电图（EEG）信号进行分解和重构。EEG信号可表示为特征函数模式和主坐标分量的组合。特征函数模式是空间变量的确定性函数，它代表电极的空间信息。而主坐标分量是时间变量的随机函数，它代表EEG的实时信号。因此，为EEG信号的特征向量空间压缩和任意随机头皮位置的电势值预测指出了一种新方法。还讨论了进一步研究的方向。
