Li Ruijiang, Principe Jose C, Bradley Margaret, Ferrari Vera
Computational NeuroEngineering Lab, Electrical and Computer Engineering Department, University of Florida, Gainesville, FL 32611, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2007;2007:5206-9. doi: 10.1109/IEMBS.2007.4353515.
Most spatiotemporal filtering methods for the problem of single-trial event-related potentials (ERP) estimation relies on the analysis of the second-order statistics (SOS) of electroencephalograph (EEG) data. Due to the noisy nature of EEG, these methods often suffer from the outliers in EEG. We combine a recently proposed spatiotemporal filtering method with the maximum correntropy criterion (MCC) for the single-trial estimation of the ERP amplitude. Study with real cognitive ERP data shows the robustness of the method with reduced estimation variance.
大多数用于单次试验事件相关电位（ERP）估计问题的时空滤波方法依赖于对脑电图（EEG）数据的二阶统计量（SOS）进行分析。由于EEG具有噪声特性，这些方法经常受到EEG中异常值的影响。我们将最近提出的一种时空滤波方法与最大相关熵准则（MCC）相结合，用于ERP幅度的单次试验估计。对真实认知ERP数据的研究表明了该方法的稳健性，其估计方差有所降低。
