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首页> 外文会议>Telecommunications and Signal Processing (TSP), 2012 35th International Conference on >Noise cancelation of epileptic interictal EEG data based on generalized eigenvalue decomposition
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Noise cancelation of epileptic interictal EEG data based on generalized eigenvalue decomposition

机译：基于广义特征值分解的癫痫发作性脑电数据噪声消除

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摘要

Denoising is an important preprocessing stage in some Electroencephalography (EEG) applications such as epileptic source localization. In this paper, we propose a new algorithm for denoising the interictal EEG data. The proposed algorithm is based on Generalized Eigenvalue Decomposition of two covariance matrices of the observations. Since one of these matrices is related to the spike durations, we should estimate the occurrence time of the spike peaks and the exact spike durations. To this end, we propose a spike detection algorithm which is based on the available spike detection methods. The comparison of the results of the proposed algorithm with the results of two well-known ICA algorithms (CoM2 and SOBI) shows that the proposed algorithm denoise data as accurately as these ICA algorithms. The advantage of the proposed technique appears in terms of numerical complexity. The results also show that the proposed algorithm is considerably faster than these ICA algorithms.
机译：在某些脑电图（EEG）应用（例如癫痫源定位）中，降噪是重要的预处理阶段。在本文中，我们提出了一种用于消除脑电数据的新算法。所提出的算法基于观测值的两个协方差矩阵的广义特征值分解。由于这些矩阵之一与峰值持续时间有关，我们应该估计峰值峰值的发生时间和确切的峰值持续时间。为此，我们提出了一种基于可用尖峰检测方法的尖峰检测算法。将该算法的结果与两种著名的ICA算法（CoM2和SOBI）的结果进行比较，结果表明，该算法对数据的去噪与这些ICA算法一样准确。所提出的技术的优点在于数值复杂性。结果还表明，所提出的算法比这些ICA算法要快得多。

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