Kristensen Emmanuelle, Rivet Bertrand, Guérin-Dugué Anne
Univ. Grenoble Alpes, GIPSA-Lab, F-38000 Grenoble France CNRS, GIPSA-Lab, F-38000 Grenoble France; Univ. Grenoble Alpes, GIPSA-Lab, 11 rue des Mathématiques Grenoble Campus, BP 46, 38000 Grenoble France.
J Eye Mov Res. 2017 Oct 7;10(1):JEMR-10-1. doi: 10.16910/jemr.10.1.7.
The Eye Fixation Related Potential (EFRP) estimation is the average of EEG signals across epochs at ocular fixation onset. Its main limitation is the overlapping issue. Inter Fixation Intervals (IFI) - typically around 300 ms in the case of unrestricted eye movement- depend on participants' oculomotor patterns, and can be shorter than the latency of the components of the evoked potential. If the duration of an epoch is longer than the IFI value, more than one fixation can occur, and some overlapping between adjacent neural responses ensues. The classical average does not take into account either the presence of several fixations during an epoch or overlapping. The Adjacent Response algorithm (ADJAR), which is popular for event-related potential estimation, was compared to the General Linear Model (GLM) on a real dataset from a conjoint EEG and eye-tracking experiment to address the overlapping issue. The results showed that the ADJAR algorithm was based on assumptions that were too restrictive for EFRP estimation. The General Linear Model appeared to be more robust and efficient. Different configurations of this model were compared to estimate the potential elicited at image onset, as well as EFRP at the beginning of exploration. These configurations took into account the overlap between the event-related potential at stimulus presentation and the following EFRP, and the distinction between the potential elicited by the first fixation onset and subsequent ones. The choice of the General Linear Model configuration was a tradeoff between assumptions about expected behavior and the quality of the EFRP estimation: the number of different potentials estimated by a given model must be controlled to avoid erroneous estimations with large variances.
眼注视相关电位（EFRP）估计是在眼注视开始时各时段脑电图信号的平均值。其主要局限性在于重叠问题。注视间隔（IFI）——在无限制眼球运动的情况下通常约为300毫秒——取决于参与者的眼球运动模式，并且可能短于诱发电位成分的潜伏期。如果一个时段的持续时间长于IFI值，可能会出现不止一次注视，相邻神经反应之间就会产生一些重叠。传统的平均值既没有考虑一个时段内存在多个注视的情况，也没有考虑重叠情况。在一个结合脑电图和眼动追踪实验的真实数据集上，将用于事件相关电位估计的常用相邻反应算法（ADJAR）与通用线性模型（GLM）进行了比较，以解决重叠问题。结果表明，ADJAR算法基于的假设对EFRP估计来说限制过严。通用线性模型似乎更稳健、更有效。对该模型的不同配置进行了比较，以估计图像开始时诱发的电位以及探索开始时的EFRP。这些配置考虑了刺激呈现时的事件相关电位与随后的EFRP之间的重叠，以及第一次注视开始诱发的电位与后续注视诱发的电位之间的区别。通用线性模型配置的选择是在关于预期行为的假设与EFRP估计质量之间进行权衡：必须控制给定模型估计的不同电位的数量，以避免出现方差大的错误估计。
