Automatic removal of eye movement and blink artifacts from EEG data using blind component separation
Automatic removal of eye movement and blink artifacts from EEG data using blind component separation
复制标题
DOI：
10.1111/j.1469-8986.2003.00141.x
复制
发表时间：
2004-03-01
期刊：
影响因子：
3.7
通讯作者：
Kutas, M
中科院分区：
文献类型：
--
作者：
Joyce, CA;Gorodnitsky, IF;Kutas, M
来自眼球运动和眨眼的信号可能比大脑产生的电位大几个数量级，并且是脑电图（EEG）数据中伪影的主要来源之一。拒绝受污染的试验会导致大量数据丢失，限制眼球运动/眨眼限制了可能的实验设计。影响研究中的认知过程。提出了一种基于盲源分离的脑电信号眼电伪迹自动去除方法。BBS是一种信号处理方法。独立成分分析（伊卡）。与先前探索的基于ICA的伪影去除方法相比，该方法是自动化的。此外，本文描述的BSS算法可以以高准确度隔离相关的眼电分量。虽然重点是消除EEG数据中的眼伪影，但该方法可以扩展到EEG污染的其他来源，例如心脏信号、环境噪声和电极漂移，并适用于脑磁图（MEG）数据（EEG的磁相关）。
Signals from eye movements and blinks can be orders of magnitude larger than brain-generated electrical potentials and are one of the main sources of artifacts in electroencephalographic (EEG) data. Rejecting contaminated trials causes substantial data loss, and restricting eye movements/blinks limits the experimental designs possible and may. impact the cognitive processes under investigation. This article presents a method based on blind source separation (BSS) for automatic removal of electroocular artifacts from EEG data. BBS is a signal-processing methodology that. includes independent component analysis (ICA). In contrast to previously explored ICA-based methods for artifact removal, this method is automated. Moreover, the BSS algorithm described herein can isolate correlated electroocular components with a high degree of accuracy. Although the focus is on eliminating ocular artifacts in EEG data, the approach can be extended to other sources of EEG contamination such as cardiac signals, environmental noise, and electrode drift, and adapted for use with magnetoencephalographic (MEG) data, a magnetic correlate of EEG.