Functional Brain Mapping Lab, University Hospital and Faculty of Medicine, Geneva, Switzerland.
EEG and Epilepsy Unit, Neurology Clinic, University Hospital, Geneva, Switzerland; Functional Brain Mapping Lab, University Hospital and Faculty of Medicine, Geneva, Switzerland.
Neuroimage. 2014 Aug 1;96:106-16. doi: 10.1016/j.neuroimage.2014.04.002. Epub 2014 Apr 12.
Relating measures of electroencephalography (EEG) back to the underlying sources is a long-standing inverse problem. Here we propose a new method to estimate the EEG sources of identified electrophysiological states that represent spontaneous activity, or are evoked by a stimulus, or caused by disease or disorder. Our method has the unique advantage of seamlessly integrating a statistical significance of the source estimate while efficiently eliminating artifacts (e.g., due to eye blinks, eye movements, bad electrodes). After determining the electrophysiological states in terms of stable topographies using established methods (e.g.: ICA, PCA, k-means, epoch average), we propose to estimate these states' time courses through spatial regression of a General Linear Model (GLM). These time courses are then used to find EEG sources that have a similar time-course (using temporal regression of a second GLM). We validate our method using both simulated and experimental data. Simulated data allows us to assess the difference between source maps obtained by the proposed method and those obtained by applying conventional source imaging of the state topographies. Moreover, we use data from 7 epileptic patients (9 distinct epileptic foci localized by intracranial EEG) and 2 healthy subjects performing an eyes-open/eyes-closed task to elicit activity in the alpha frequency range. Our results indicate that the proposed EEG source imaging method accurately localizes the sources for each of the electrical brain states. Furthermore, our method is particularly suited for estimating the sources of EEG resting states or otherwise weak spontaneous activity states, a problem not adequately solved before.
将脑电图（EEG）的测量结果与潜在的源相关联是一个长期存在的反问题。在这里，我们提出了一种新的方法来估计识别出的电生理状态的 EEG 源，这些状态代表自发活动，或者是由刺激引起的，或者是由疾病或障碍引起的。我们的方法具有独特的优势，即在有效地消除伪影（例如，由于眨眼、眼球运动、电极不良等）的同时，无缝地整合了源估计的统计显著性。在使用已建立的方法（例如：ICA、PCA、k-均值、epoch 平均）根据稳定的拓扑结构确定电生理状态之后，我们建议通过广义线性模型（GLM）的空间回归来估计这些状态的时间过程。然后，这些时间过程用于找到具有相似时间过程的 EEG 源（使用第二个 GLM 的时间回归）。我们使用模拟数据和实验数据验证了我们的方法。模拟数据使我们能够评估通过所提出的方法获得的源图与通过应用状态拓扑的常规源成像获得的源图之间的差异。此外，我们使用来自 7 名癫痫患者（通过颅内 EEG 定位的 9 个不同癫痫灶）和 2 名健康受试者执行睁眼/闭眼任务的数据，以在 alpha 频带诱发活动。我们的结果表明，所提出的 EEG 源成像方法能够准确地定位每个电生理状态的源。此外，我们的方法特别适合估计 EEG 静息状态或其他自发活动较弱状态的源，这是以前没有很好解决的问题。
