PDF(Pubmed)
Abstract：
Ocular artifact has long been viewed as an impediment to the interpretation of electroencephalogram (EEG) signals in basic and applied research. Today, the use of blind source separation (BSS) methods, including independent component analysis (ICA) and second-order blind identification (SOBI), is considered an essential step in improving the quality of neural signals. Recently, we introduced a method consisting of SOBI and a discriminant and similarity (DANS)-based identification method, capable of identifying and extracting eye movement-related components. These recovered components can be localized within ocular structures with a high goodness of fit (>95%). This raised the possibility that such EEG-derived SOBI components may be used to build predictive models for tracking gaze position.
As proof of this new concept, we designed an EEG-based virtual eye-tracker (EEG-VET) for tracking eye movement from EEG alone. The EEG-VET is composed of a SOBI algorithm for separating EEG signals into different components, a DANS algorithm for automatically identifying ocular components, and a linear model to transfer ocular components into gaze positions.
The prototype of EEG-VET achieved an accuracy of 0.920° and precision of 1.510° of a visual angle in the best participant, whereas an average accuracy of 1.008° ± 0.357° and a precision of 2.348° ± 0.580° of a visual angle across all participants (N = 18).
This work offers a novel approach that readily co-registers eye movement and neural signals from a single-EEG recording, thus increasing the ease of studying neural mechanisms underlying natural cognition in the context of free eye movement.
As proof of this new concept, we designed an EEG-based virtual eye-tracker (EEG-VET) for tracking eye movement from EEG alone. The EEG-VET is composed of a SOBI algorithm for separating EEG signals into different components, a DANS algorithm for automatically identifying ocular components, and a linear model to transfer ocular components into gaze positions.
The prototype of EEG-VET achieved an accuracy of 0.920° and precision of 1.510° of a visual angle in the best participant, whereas an average accuracy of 1.008° ± 0.357° and a precision of 2.348° ± 0.580° of a visual angle across all participants (N = 18).
This work offers a novel approach that readily co-registers eye movement and neural signals from a single-EEG recording, thus increasing the ease of studying neural mechanisms underlying natural cognition in the context of free eye movement.
摘要：
背景：眼部伪影长期以来一直被视为基础和应用研究中解释脑电图（EEG）信号的障碍。今天,使用盲源分离(BSS)方法，包括独立成分分析(ICA)和二阶盲识别(SOBI)，被认为是改善神经信号质量的重要步骤。最近,我们介绍了一种由SOBI和基于判别和相似度(DANS)的识别方法组成的方法，能够识别和提取与眼睛运动相关的成分。这些回收的组件可以以高拟合优度(>95%)定位在眼部结构内。这提高了这种EEG导出的SOBI分量可以用于构建用于跟踪注视位置的预测模型的可能性。
方法：作为这个新概念的证明，我们设计了一种基于EEG的虚拟眼睛跟踪器（EEG-VET），用于仅从EEG跟踪眼球运动。EEG-VET由SOBI算法组成，用于将EEG信号分离为不同的分量，用于自动识别眼部组件的DANS算法，和一个线性模型，将眼部成分转移到凝视位置。
结果：EEG-VET的原型在最佳参与者中实现了0.920°的精度和1.510°的视角精度，而所有参与者的平均精确度为1.008°±0.357°，视角精确度为2.348°±0.580°（N=18）。
结论：这项工作提供了一种新颖的方法，可以轻松地从单个EEG记录中记录眼球运动和神经信号。从而增加了在自由眼动的背景下研究自然认知的神经机制的便利性。
方法：作为这个新概念的证明，我们设计了一种基于EEG的虚拟眼睛跟踪器（EEG-VET），用于仅从EEG跟踪眼球运动。EEG-VET由SOBI算法组成，用于将EEG信号分离为不同的分量，用于自动识别眼部组件的DANS算法，和一个线性模型，将眼部成分转移到凝视位置。
结果：EEG-VET的原型在最佳参与者中实现了0.920°的精度和1.510°的视角精度，而所有参与者的平均精确度为1.008°±0.357°，视角精确度为2.348°±0.580°（N=18）。
结论：这项工作提供了一种新颖的方法，可以轻松地从单个EEG记录中记录眼球运动和神经信号。从而增加了在自由眼动的背景下研究自然认知的神经机制的便利性。
