A Riemannian Modification of Artifact Subspace Reconstruction for EEG Artifact Handling
A Riemannian Modification of Artifact Subspace Reconstruction for EEG Artifact Handling
复制标题
DOI：
10.3389/fnhum.2019.00141
复制
发表时间：
2019-04-26
影响因子：
2.9
通讯作者：
Debener, Stefan
中科院分区：
文献类型：
--
作者：
Blum, Sarah;Jacobsen, Nadine S. J.;Debener, Stefan
伪影子空间重建（ASR）是一种用于在线或离线校正包含多通道脑电图（EEG）记录的伪影的自适应方法。它在协方差矩阵上反复计算主成分分析（PCA），根据构件子空间中的统计特性检测伪影。我们通过使用黎曼几何对协方差矩阵进行处理来调整现有的ASR实现。使用智能手机在室外和室内条件下记录的脑电图数据进行评估（N = 27）。将原始ASR与riemanian ASR （rASR）在眨眼次数减少（敏感性）、视觉诱发电位（特异性）改善和计算时间（效率）三个性能指标上进行直接比较。与ASR相比，我们的rASR算法在所有三个指标上都表现良好。结果表明，rASR方法适用于实验室和野外采集的多路EEG数据的离线和在线校正。
Artifact Subspace Reconstruction (ASR) is an adaptive method for the online or offline correction of artifacts comprising multichannel electroencephalography (EEG) recordings. It repeatedly computes a principal component analysis (PCA) on covariance matrices to detect artifacts based on their statistical properties in the component subspace. We adapted the existing ASR implementation by using Riemannian geometry for covariance matrix processing. EEG data that were recorded on smartphone in both outdoors and indoors conditions were used for evaluation (N = 27). A direct comparison between the original ASR and Riemannian ASR (rASR) was conducted for three performance measures: reduction of eye-blinks (sensitivity), improvement of visual-evoked potentials (VEPs) (specificity), and computation time (efficiency). Compared to ASR, our rASR algorithm performed favorably on all three measures. We conclude that rASR is suitable for the offline and online correction of multichannel EEG data acquired in laboratory and in field conditions.