Empirical Mode Decomposition Method for MEG Phantom Data Analysis
Empirical Mode Decomposition Method for MEG Phantom Data Analysis
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DOI：
10.1142/s0218126609005794
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发表时间：
2009-12
期刊：
影响因子：
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通讯作者：
Ju-Hong Yang;Yuki Saito;Qi-Wei Shi;Jianting Cao;Toshihisa Tanaka;T. Takeda
中科院分区：
文献类型：
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作者：
Ju-Hong Yang;Yuki Saito;Qi-Wei Shi;Jianting Cao;Toshihisa Tanaka;T. Takeda
脑磁图 (MEG) 是一种强大的非侵入性技术，用于以高时间分辨率测量人类大脑活动。研究脑磁图数据分析的动机是从现实世界的测量数据中提取本质特征，并将其表示为与人脑功能相对应的特征。这通常取决于如何减少测量中的高电平噪声。本文提出了一种基于经验模态分解（EMD）和独立成分分析（ICA）方法的新型多级 MEG 数据分析方法，用于特征提取。此外，还研究了 EMD 和 ICA 算法来分析体模实验记录的 MEG 单次试验数据。分析结果说明了与 ICA 方法相关的 EMD 和等效电流偶极子拟合方法的源定位在高水平降噪方面的有效性和高性能。
Magnetoencephalography (MEG) is a powerful and non-invasive technique for measuring human brain activity with a high temporal resolution. The motivation for studying MEG data analysis is to extract the essential features from real-world measured data and represent them corresponding to the human brain functions. This usually depends on how to reduce a high level noise from the measurement. In this paper, a novel multistage MEG data analysis method based on the empirical mode decomposition (EMD) and independent component analysis (ICA) approaches is proposed for the feature extraction. Moreover, EMD and ICA algorithms are investigated for analyzing the MEG single-trial data which is recorded from the experiment of phantom. The analyzed results are presented to illustrate the effectiveness and high performance both in high level noise reduction by EMD associated with ICA approach and source localization by equivalent current dipole fitting method.