Multivariate temporal dictionary learning for EEG
Multivariate temporal dictionary learning for EEG
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DOI：
10.1016/j.jneumeth.2013.02.001
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发表时间：
2013-04-30
影响因子：
3
通讯作者：
Mars, J. I.
中科院分区：
文献类型：
--
作者：
Barthelemy, Q.;Gouy-Pailler, C.;Mars, J. I.
本文讨论了如何有效地表示脑电图（EEG）信号。经典方法使用固定的Gabor字典来分析脑电信号，本文提出了一种数据驱动的方法来获得自适应字典。为了达到有效的字典学习，需要适当的空间和时间建模。在空间多元模型中考虑了通道间的联系，在时间模型中使用了平移不变性。多元学习核具有信息性（少量原子编码大量能量）和可解释性（原子可以具有生理意义）。利用真实脑电数据，通过学习字典的代表性和空间灵活性来衡量，该方法优于经典的Gabor字典的多通道匹配追踪方法。此外，字典学习可以捕获可解释的模式：这种能力在实际数据中得到了说明，学习了P300诱发电位。(C) 2013 Elsevier B.V.版权所有
This article addresses the issue of representing electroencephalographic (EEG) signals in an efficient way. While classical approaches use a fixed Gabor dictionary to analyze EEG signals, this article proposes a data-driven method to obtain an adapted dictionary. To reach an efficient dictionary learning, appropriate spatial and temporal modeling is required. Inter-channels links are taken into account in the spatial multivariate model, and shift-invariance is used for the temporal model. Multivariate learned kernels are informative (a few atoms code plentiful energy) and interpretable (the atoms can have a physiological meaning). Using real EEG data, the proposed method is shown to outperform the classical multichannel matching pursuit used with a Gabor dictionary, as measured by the representative power of the learned dictionary and its spatial flexibility. Moreover, dictionary learning can capture interpretable patterns: this ability is illustrated on real data, learning a P300 evoked potential. (C) 2013 Elsevier B.V. All rights reserved.