A hierarchical Bayesian approach for learning sparse spatio-temporal decompositions of multichannel EEG.
A hierarchical Bayesian approach for learning sparse spatio-temporal decompositions of multichannel EEG.
复制标题
DOI：
10.1016/j.neuroimage.2011.03.032
复制
发表时间：
2011-06-15
期刊：
影响因子：
5.7
通讯作者：
Brown, Emery N.
中科院分区：
文献类型：
--
作者：
Wu, Wei;Chen, Zhe;Gao, Shangkai;Brown, Emery N.
关键词：
多通道脑电图（EEG）提供了一种非侵入性的工具来探索大脑活动的时空动力学。由于EEG记录由多个试验组成，忽略数据中试验间变化的传统信号处理方法可能无法准确估计潜在的时空大脑模式。此外，这种试验间变异性本身的精确表征在建立大脑活动和行为之间的关系方面具有很高的科学价值。在本文中，一个统计建模框架的学习时空分解的多次试验记录在两个对比实验条件下的EEG数据。通过将源信号的方差建模为跨试验变化的随机变量，所提出的两阶段分层贝叶斯模型能够以稀疏的方式捕获数据中的试验间幅度变化，其中可以获得数据的简约表示。提出了一种变分贝叶斯（VB）算法用于层次模型的统计推断。通过对合成和真实的EEG数据的分析，验证了所提出的建模框架的有效性。在模拟研究中，我们表明，即使在低信噪比下，我们的方法也能够高精度地恢复潜在的时空模式和源振幅在试验中的演变;在两个脑机接口（BCI）数据集上，我们表明我们的VB算法可以提取生理上有意义的时空模式，并比其他两种广泛使用的算法做出更准确的预测：公共空间模式（CSP）算法和用于独立分量分析（伊卡）的Infomax算法。结果表明，我们的统计建模框架可以作为一个强大的工具，用于提取大脑模式，表征试验到试验的大脑动力学，并通过利用数据中有用的结构解码大脑状态。
Multichannel electroencephalography (EEG) offers a non-invasive tool to explore spatio-temporal dynamics of brain activity. With EEG recordings consisting of multiple trials, traditional signal processing approaches that ignore inter-trial variability in the data may fail to accurately estimate the underlying spatio-temporal brain patterns. Moreover, precise characterization of such inter-trial variability per se can be of high scientific value in establishing the relationship between brain activity and behavior. In this paper, a statistical modeling framework is introduced for learning spatiotemporal decomposition of multiple-trial EEG data recorded under two contrasting experimental conditions. By modeling the variance of source signals as random variables varying across trials, the proposed two-stage hierarchical Bayesian model is able to capture inter-trial amplitude variability in the data in a sparse way where a parsimonious representation of the data can be obtained. A variational Bayesian (VB) algorithm is developed for statistical inference of the hierarchical model. The efficacy of the proposed modeling framework is validated with the analysis of both synthetic and real EEG data. In the simulation study we show that even at low signal-to-noise ratios our approach is able to recover with high precision the underlying spatiotemporal patterns and the evolution of source amplitude across trials; on two brain-computer interface (BCI) data sets we show that our VB algorithm can extract physiologically meaningful spatio-temporal patterns and make more accurate predictions than other two widely used algorithms: the common spatial patterns (CSP) algorithm and the Infomax algorithm for independent component analysis (ICA). The results demonstrate that our statistical modeling framework can serve as a powerful tool for extracting brain patterns, characterizing trial-to-trial brain dynamics, and decoding brain states by exploiting useful structures in the data.
登录
查看更多内容
DOI：
10.1109/tnsre.2006.875642
发表时间：
2006-06-01
影响因子：
4.9
作者：
Blankertz, Benjamin;Mueller, Klaus-Robert;Birbaumer, Niels
通讯作者：
Birbaumer, Niels
影响因子：
5.7
作者：
Friman, O;Borga, M;Knutsson, H
通讯作者：
Knutsson, H
影响因子：
5.4
作者：
Belouchrani, A;AbedMeraim, K;Moulines, E
通讯作者：
Moulines, E
影响因子：
5.4
作者：
Cotter, SF;Rao, BD;Kreutz-Delgado, K
通讯作者：
Kreutz-Delgado, K
影响因子：
3.7
作者：
FUKUNAGA, K;KOONTZ, WLG
通讯作者：
KOONTZ, WLG