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角膜视网膜偶极子和眼睑相关的眼伪迹可以在脑电图和脑磁图信号中离线和在线进行校正。

Corneo-retinal-dipole and eyelid-related eye artifacts can be corrected offline and online in electroencephalographic and magnetoencephalographic signals.

机构信息

Institute of Neural Engineering, Graz University of Technology, Graz, 8010, Styria, Austria.

Department of Neurological Diagnosis and Restoration, Osaka University Graduate School of Medicine, Suita, Osaka, Japan; Department of Neurosurgery, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.

出版信息

Neuroimage. 2020 Sep;218:117000. doi: 10.1016/j.neuroimage.2020.117000. Epub 2020 Jun 1.

Abstract

Eye movements and blinks contaminate electroencephalographic (EEG) and magnetoencephalographic (MEG) activity. As the eye moves, the corneo-retinal dipole (CRD) and eyelid introduce potential/field changes in the M/EEG activity. These eye artifacts can affect a brain-computer interface and thereby impinge on neurofeedback quality. Here, we introduce the sparse generalized eye artifact subspace subtraction (SGEYESUB) algorithm that can correct these eye artifacts offline and in real time. We provide an open source reference implementation of the algorithm and the paradigm to obtain calibration data. Once the algorithm is fitted to calibration data (approx. 5 ​min), the eye artifact correction reduces to a matrix multiplication. We compared SGEYESUB with 4 state-of-the-art algorithms using M/EEG activity of 69 participants. SGEYESUB achieved the best trade-off between correcting the eye artifacts and preserving brain activity. Residual correlations between the corrected M/EEG channels and the eye artifacts were below 0.1. Error-related and movement-related cortical potentials were attenuated by less than 0.5 ​μV. Our results furthermore demonstrate that CRD and eyelid-related artifacts can be assumed to be stationary for at least 1-1.5 ​h, validating the feasibility of our approach in offline and online eye artifact correction.

摘要

眼球运动和眨眼会污染脑电图(EEG)和脑磁图(MEG)活动。当眼睛移动时,角膜视网膜偶极子(CRD)和眼睑会在 M/EEG 活动中引入电势/场变化。这些眼伪迹会影响脑机接口,并因此影响神经反馈质量。在这里,我们引入了稀疏广义眼伪迹子空间减法(SGEYESUB)算法,该算法可以离线和实时校正这些眼伪迹。我们提供了算法和获得校准数据的范例的开源参考实现。一旦算法拟合到校准数据(大约 5 分钟),眼伪迹校正就简化为矩阵乘法。我们使用 69 名参与者的 M/EEG 活动将 SGEYESUB 与 4 种最先进的算法进行了比较。SGEYESUB 在校正眼伪迹和保留脑活动之间取得了最佳的折衷。校正后的 M/EEG 通道与眼伪迹之间的残余相关性低于 0.1。错误相关和运动相关皮质电位的衰减幅度小于 0.5 μV。我们的结果还表明,CRD 和眼睑相关伪迹至少可以假定为静止状态 1-1.5 小时,验证了我们的离线和在线眼伪迹校正方法的可行性。

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