School of Fundamental Science and Technology, Graduate School of Keio University, Kanagawa, 223-8522, Japan; Division of System Neuroscience, National Institute for Physiological Sciences, Aichi, 444-8585, Japan.
Graduate School of Frontier Biosciences, Osaka University, Osaka, 565-0871, Japan; National Institute of Information and Communications Technology, Center for Information and Neural Networks, Osaka, 565-0871, Japan; Japan Society for the Promotion of Science, Tokyo, 102-0083, Japan.
J Neurosci Methods. 2021 Apr 1;353:109089. doi: 10.1016/j.jneumeth.2021.109089. Epub 2021 Jan 27.
Oscillations in the resting-state scalp electroencephalogram (EEG) represent various intrinsic brain activities. One of the characteristic EEG oscillations is the sensorimotor rhythm (SMR)-with its arch-shaped waveform in alpha- and betabands-that reflect sensorimotor activity. The representation of sensorimotor activity by the SMR depends on the signal-to-noise ratio achieved by EEG spatial filters.
We employed simultaneous recording of EEG and functional magnetic resonance imaging, and 10-min resting-state brain activities were recorded in 19 healthy volunteers. To compare the EEG spatial-filtering methods commonly used for extracting sensorimotor cortical activities, we assessed nine different spatial-filters: a default reference of EEG amplifier system, a common average reference (CAR), small-, middle- and large-Laplacian filters, and four types of bipolar manners (C3-Cz, C3-F3, C3-P3, and C3-T7). We identified the brain region that correlated with the EEG-SMR power obtained after each spatial-filtering method was applied. Subsequently, we calculated the proportion of the significant voxels in the sensorimotor cortex as well as the sensorimotor occupancy in all significant regions to examine the sensitivity and specificity of each spatial-filter.
The CAR and large-Laplacian spatial-filters were superior at improving the signal-to-noise ratios for extracting sensorimotor activity from the EEG-SMR signal.
Our results are consistent with the spatial-filter selection to extract task-dependent activation for better control of EEG-SMR-based interventions. Our approach has the potential to identify the optimal spatial-filter for EEG-SMR.
Evaluating spatial-filters for extracting spontaneous sensorimotor activity from the EEG is a useful procedure for constructing more effective EEG-SMR-based interventions.
静息状态头皮脑电图（EEG）中的波动代表各种内在的大脑活动。特征性 EEG 波动之一是感觉运动节律（SMR）-其在 alpha 和 beta 波段中呈拱形波形-反映感觉运动活动。SMR 对感觉运动活动的表示取决于 EEG 空间滤波器实现的信噪比。
我们采用 EEG 和功能磁共振成像同步记录，在 19 名健康志愿者中记录了 10 分钟的静息状态大脑活动。为了比较常用于提取感觉运动皮质活动的 EEG 空间滤波方法，我们评估了九种不同的空间滤波器：EEG 放大器系统的默认参考、常见平均参考（CAR）、小、中、大拉普拉斯滤波器，以及四种双极方式（C3-Cz、C3-F3、C3-P3 和 C3-T7）。我们确定了与应用每种空间滤波方法后获得的 EEG-SMR 功率相关的大脑区域。随后，我们计算了在感觉运动皮层中有意义的体素的比例以及所有有意义区域中的感觉运动占有率，以检查每种空间滤波器的灵敏度和特异性。
CAR 和大拉普拉斯滤波器在提高从 EEG-SMR 信号中提取感觉运动活动的信噪比方面表现出色。
我们的结果与选择空间滤波器以提取任务相关激活以更好地控制基于 EEG-SMR 的干预措施一致。我们的方法有可能确定最佳的 EEG-SMR 空间滤波器。
评估从 EEG 中提取自发感觉运动活动的空间滤波器是构建更有效的基于 EEG-SMR 的干预措施的有用程序。
