Blinowska K J, Czerwosz L T, Drabik W, Franaszczuk P J, Ekiert H
Electroencephalogr Clin Neurophysiol. 1981 Jun;51(6):650-8. doi: 10.1016/0013-4694(81)90209-1.
A program for automatic evaluation of EEG spectra, providing considerable reduction of data, was devised. Artefacts were eliminated in two steps: first, the longer duration eye movement artefacts were removed by a fast and simple 'moving integral' methods, then occasional spikes were identified by means of a detection function defined in the formalism of the autoregressive (AR) model. The evaluation of power spectra was performed by means of an FFT and autoregressive representation, which made possible the comparison of both methods. The spectra obtained by means of the AR model had much smaller statistical fluctuations and better resolution, enabling us to follow the time changes of the EEG pattern. Another advantage of the autoregressive approach was the parametric description of the signal. This last property appeared to be essential in distinguishing the changes in the EEG pattern. In a drug study the application of the coefficients of the AR model as input parameters in the discriminant analysis, instead of arbitrary chosen frequency bands, brought a significant improvement in distinguishing the effects of the medication. The favourable properties of the AR model are connected with the fact that the above approach fulfils the maximum entropy principle. This means that the method describes in a maximally consistent way the available information and is free from additional assumptions, which is not the case for the FFT estimate.
设计了一个用于自动评估脑电图频谱的程序，该程序可大幅减少数据量。伪迹分两步消除：首先，通过快速简单的“移动积分”方法去除持续时间较长的眼动伪迹，然后通过自回归（AR）模型形式定义的检测函数识别偶尔出现的尖峰。通过快速傅里叶变换（FFT）和自回归表示法进行功率谱评估，这使得两种方法的比较成为可能。通过AR模型获得的频谱具有小得多的统计波动和更好的分辨率，使我们能够追踪脑电图模式的时间变化。自回归方法的另一个优点是信号的参数描述。最后这个特性在区分脑电图模式的变化方面似乎至关重要。在一项药物研究中，将AR模型的系数作为判别分析的输入参数，而不是任意选择的频段，在区分药物效果方面带来了显著改善。AR模型的良好特性与上述方法符合最大熵原理这一事实有关。这意味着该方法以最大程度一致的方式描述可用信息，并且没有额外的假设，而FFT估计并非如此。
