Timely detection of dynamical change in scalp EEG signals
Timely detection of dynamical change in scalp EEG signals
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DOI：
10.1063/1.1312369
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发表时间：
2000-12-01
期刊：
影响因子：
2.9
通讯作者：
Gailey, PC
中科院分区：
文献类型：
--
作者：
Hively, LM;Protopopescu, VA;Gailey, PC
我们提出了一个强大的，独立于模型的技术，量化的动态变化的非线性时间序列数据。在构造了时间窗数据集吸引子上相空间点的离散密度分布后，我们通过L-1-距离和chi（2）统计量度量了密度分布之间的不相似性。新措施的鉴别力首先测试由邦达连科“合成大脑”模型产生的数据。我们还比较了传统的非线性措施和新的相异措施检测头皮EEG数据的动态变化。结果表明，新的措施相比，传统的非线性措施的鲁棒性和及时的变化动态鉴别器的明显优势。(C)2000年美国物理学会。[S1054-1500（00）00504-8]。
We present a robust, model-independent technique for quantifying changes in the dynamics underlying nonlinear time-serial data. After constructing discrete density distributions of phase-space points on the attractor for time-windowed data sets, we measure the dissimilarity between density distributions via L-1-distance and chi (2) statistics. The discriminating power of the new measures is first tested on data generated by the Bondarenko "synthetic brain" model. We also compare traditional nonlinear measures and the new dissimilarity measures to detect dynamical change in scalp EEG data. The results demonstrate a clear superiority of the new measures in comparison to traditional nonlinear measures as robust and timely discriminators of changing dynamics. (C) 2000 American Institute of Physics. [S1054-1500(00)00504-8].