包括时间跟踪在内的多通道脑电图数据的多维平行因子2成分分析。
Multi-dimensional PARAFAC2 component analysis of multi-channel EEG data including temporal tracking.
作者信息
Weis Martin, Jannek Dunja, Roemer Florian, Guenther Thomas, Haardt Martin, Husar Peter
机构信息
Biosignal Processing Group, Ilmenau University of Technology, Gustav-Kirchhoff Str. 2, D-98684, Germany.
出版信息
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:5375-8. doi: 10.1109/IEMBS.2010.5626484.
The identification of signal components in electroencephalographic (EEG) data originating from neural activities is a long standing problem in neuroscience. This area has regained new attention due to the possibilities of multi-dimensional signal processing. In this work we analyze measured visual-evoked potentials on the basis of the time-varying spectrum for each channel. Recently, parallel factor (PARAFAC) analysis has been used to identify the signal components in the space-time-frequency domain. However, the PARAFAC decomposition is not able to cope with components appearing time-shifted over the different channels. Furthermore, it is not possible to track PARAFAC components over time. In this contribution we derive how to overcome these problems by using the PARAFAC2 model, which renders it an attractive approach for processing EEG data with highly dynamic (moving) sources.
识别源自神经活动的脑电图（EEG）数据中的信号成分是神经科学中一个长期存在的问题。由于多维信号处理的可能性，该领域重新受到了关注。在这项工作中，我们基于每个通道的时变频谱分析测量到的视觉诱发电位。最近，平行因子（PARAFAC）分析已被用于在时空频域中识别信号成分。然而，PARAFAC分解无法处理在不同通道上出现时间偏移的成分。此外，不可能随时间跟踪PARAFAC成分。在本论文中，我们推导了如何通过使用PARAFAC2模型来克服这些问题，这使其成为处理具有高度动态（移动）源的EEG数据的一种有吸引力的方法。
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