Durka Piotr J, Matysiak Artur, Montes Eduardo Martínez, Sosa Pedro Valdés, Blinowska Katarzyna J
Department of Biomedical Physics, Institute of Experimental Physics, Warsaw University, ul. Hoza 69, 00-681 Warszawa, Poland.
J Neurosci Methods. 2005 Oct 15;148(1):49-59. doi: 10.1016/j.jneumeth.2005.04.001.
We present a new approach to the preprocessing of the electroencephalographic time series for EEG inverse solutions. As the first step, EEG recordings are decomposed by multichannel matching pursuit algorithm--in this study we introduce a computationally efficient, suboptimal solution. Then, based upon the parameters of the waveforms fitted to the EEG (frequency, amplitude and duration), we choose those corresponding to the the phenomena of interest, like e.g. sleep spindles. For each structure, the corresponding weights of each channel define a topographic signature, which can be subject to an inverse solution procedure, like e.g. Loreta, used in this work. As an example, we present an automatic detection and parameterization of sleep spindles, appearing in overnight polysomnographic recordings. Inverse solutions obtained for single sleep spindles are coherent with the averages obtained for 20 overnight EEG recordings analyzed in this study, as well as with the results reported previously in literature as inter-subject averages of solutions for spectral integrals, computed on visually selected spindles.
我们提出了一种用于脑电图逆解的脑电图时间序列预处理新方法。第一步，通过多通道匹配追踪算法对脑电图记录进行分解——在本研究中，我们引入了一种计算效率高的次优解决方案。然后，根据拟合到脑电图的波形参数（频率、幅度和持续时间），我们选择那些对应于感兴趣现象的参数，例如睡眠纺锤波。对于每个结构，每个通道的相应权重定义了一个地形特征，该特征可以进行逆解程序，例如本工作中使用的洛雷塔（Loreta）方法。作为一个例子，我们展示了在夜间多导睡眠图记录中出现的睡眠纺锤波的自动检测和参数化。针对单个睡眠纺锤波获得的逆解与本研究中分析的20次夜间脑电图记录的平均值一致，也与先前文献中报道的作为视觉选择纺锤波频谱积分解的受试者间平均值的结果一致。
