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Decomposition Methods for Detailed Analysis of Content in ERP Recordings

机译:用于ERP记录内容的详细分析的分解方法

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The processes giving rise to an event related potential engage several evoked and induced oscillatory components, which reflect phase or non-phase locked activity throughout the multiple trials. The separation and identification of such components could not only serve diagnostic purposes, but also facilitate the design of brain-computer interface systems. However, the effective analysis of components is hindered by many factors including the complexity of the EEG signal and its variation over the trials. In this paper we study several measures for the identification of the nature of independent components and address the means for efficient decomposition of the rich information content embedded in the multi-channel EEG recordings associated with the multiple trials of an event-related experiment. The efficiency of the proposed methodology is demonstrated through simulated and real experiments.
机译:产生与事件相关的电位的过程涉及多个诱发和诱发的振荡成分,这些成分反映了多次试验中的相位或非相位锁定活动。这些组件的分离和识别不仅可以用于诊断目的,而且可以促进脑机接口系统的设计。然而,许多因素阻碍了对成分的有效分析,包括脑电信号的复杂性及其在试验中的变化。在本文中,我们研究了几种用于识别独立组件性质的措施,并提出了有效分解嵌入在与事件相关实验的多次试验相关的多通道EEG录音中的丰富信息内容的方法。通过模拟和实际实验证明了所提出方法的效率。

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