Georgiadis S D, Tarvainen M P, Kaskinoro K, Maksimow A, Kärki T, Jääskeläinen S, Scheinin H, Karjalainen P A
Department of Physics, University of Kuopio, Kuopio, Finland.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:5709-12. doi: 10.1109/IEMBS.2009.5332660.
A time-varying parametric spectrum estimation method for analyzing EEG dynamics is presented. EEG signals are first modeled as a time-varying auto-regressive stochastic process and the model parameters are estimated recursively with a Kalman smoother algorithm. Time-varying spectrum estimates are then obtained from the estimated parameters. The proposed method was applied to measurements collected during low dose propofol anesthesia. The method was able to detect changes of event related (de)synchronization type elicited by verbal command.
提出了一种用于分析脑电图（EEG）动态变化的时变参数谱估计方法。EEG信号首先被建模为时变自回归随机过程，然后使用卡尔曼平滑算法递归估计模型参数。接着从估计的参数中获得时变谱估计。所提出的方法应用于低剂量丙泊酚麻醉期间收集的测量数据。该方法能够检测由言语指令引发的事件相关（去）同步类型的变化。
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