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.
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。