Department of Biomedical Engineering, Tsinghua University, Beijing 100084, China; College of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China.
Department of Biomedical Engineering, Tsinghua University, Beijing 100084, China.
Neuroimage. 2014 Mar;88:319-39. doi: 10.1016/j.neuroimage.2013.11.028. Epub 2013 Dec 12.
Extraction and separation of functionally different event-related potentials (ERPs) from electroencephalography (EEG) is a long-standing problem in cognitive neuroscience. In this paper, we propose a Bayesian spatio-temporal model for estimating ERP components from multichannel EEG recorded under multiple experimental conditions. The model isolates the spatially and temporally overlapping ERP components by utilizing their phase-locking structure and the inter-condition non-stationarity structure of their amplitudes and latencies. Critically, unlike in previous multilinear algorithms, the non-phase-locked background EEGs are modeled as spatially correlated and non-isotropic signals. A variational algorithm was developed for approximate Bayesian inference of the proposed model, with the effective number of ERP components automatically determined as a part of the algorithm. The utility of the algorithm is demonstrated with applications to synthetic data and the EEG data collected from 13 subjects during a face inversion experiment. The results show that our algorithm more accurately and reliably estimates the spatio-temporal patterns, amplitudes, and latencies of the underlying ERP components in comparison with several state-of-the-art algorithms.
从脑电图 (EEG) 中提取和分离功能不同的事件相关电位 (ERPs) 是认知神经科学中的一个长期存在的问题。在本文中，我们提出了一种贝叶斯时空模型，用于从在多种实验条件下记录的多通道 EEG 中估计 ERP 成分。该模型通过利用它们的相位锁定结构以及幅度和潜伏期的条件间非平稳性结构，分离空间和时间上重叠的 ERP 成分。关键的是，与以前的多线性算法不同，非相位锁定的背景 EEG 被建模为空间相关且各向异性的信号。开发了一种变分算法来进行所提出模型的近似贝叶斯推断，其中有效 ERP 成分的数量自动确定为算法的一部分。该算法的实用性通过应用于合成数据和在面孔反转实验中从 13 个受试者收集的 EEG 数据得到了证明。结果表明，与几种最先进的算法相比，我们的算法更准确可靠地估计了潜在 ERP 成分的时空模式、幅度和潜伏期。
