Notley Scott V, Elliott Stephen J
Institute of Sound and Vibration Research, University of Southampton, Southampton, Hampshire SO17 1BJ, UK.
IEEE Trans Biomed Eng. 2003 May;50(5):594-602. doi: 10.1109/TBME.2003.810691.
This paper considers the problem of estimating the dimension of nonstationary electroencephalogram (EEG) signals and describes the implementation of an efficient algorithm to calculate a time-varying dimension estimate. The algorithm allows the practical calculation of a dimension estimate and its statistical significance over large data sets with a high temporal resolution. The method is applied to EEG recordings from patients with temporal lobe epilepsy and in one case the results of the analysis are compared with those obtained from an existing method of computing the correlation density.
本文考虑了估计非平稳脑电图（EEG）信号维度的问题，并描述了一种有效算法的实现，以计算随时间变化的维度估计值。该算法允许在具有高时间分辨率的大数据集上实际计算维度估计值及其统计显著性。该方法应用于颞叶癫痫患者的脑电图记录，并且在一个案例中，将分析结果与通过现有计算相关密度方法获得的结果进行了比较。
