一种用于单次试验事件相关电位成分估计的时空滤波方法。
A spatiotemporal filtering methodology for single-trial ERP component estimation.
作者信息
Li Ruijiang, Principe Jose C, Bradley Margaret, Ferrari Vera
机构信息
Computational NeuroEngineering Laboratory, Electrical and Computer Engineering Department, University of Florida, Gainesville, FL 32611, USA.
出版信息
IEEE Trans Biomed Eng. 2009 Jan;56(1):83-92. doi: 10.1109/TBME.2008.2002153.
A new spatiotemporal filtering method for single-trial event-related potential (ERP) estimation is proposed. Instead of attempting to model the entire ERP waveform, the method relies on modeling ERP component descriptors (amplitude and latency) thru the spatial diversity of multichannel recordings, and thus, it is tailored to extract signals in negative SNR conditions. The model allows for both amplitude and latency variability in the ERP component under investigation. The extracted ERP component is constrained through a spatial filter to have minimal distance (with respect to some metric) in the temporal domain from a user-designed template component. The spatial filter may be interpreted as a noise canceller in the spatial domain. Study with both simulated data and real cognitive ERP data shows the effectiveness of the proposed method.
提出了一种用于单次试验事件相关电位（ERP）估计的新的时空滤波方法。该方法不是试图对整个ERP波形进行建模，而是通过多通道记录的空间多样性对ERP成分描述符（幅度和潜伏期）进行建模，因此，它适用于在负信噪比条件下提取信号。该模型允许在所研究的ERP成分中存在幅度和潜伏期的变异性。通过空间滤波器对提取的ERP成分进行约束，使其在时间域中与用户设计的模板成分具有最小距离（相对于某种度量）。空间滤波器可以解释为空间域中的噪声消除器。对模拟数据和真实认知ERP数据的研究均表明了该方法的有效性。
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