凝视估计,作为一种反映个人注意力的技术,可用于残疾援助和协助医生诊断疾病,如自闭症谱系障碍(ASD),帕金森病,注意缺陷多动障碍(ADHD)。已经提出了用于注视估计的各种技术并且实现了高分辨率。在这些方法中,基于眼电图(EOG)的凝视估计,作为一种经济有效的方法,为实际应用提供了一个有前途的解决方案。
目的:在本文中,我们系统地研究了可能的EOG电极位置,这些位置在空间上分布在轨道腔周围。之后,从七个差分通道中提取用于表征来自时间频谱域的眼睛运动的生理信息的大量信息特征。
方法:要选择最佳频道和相关功能,消除不相关的信息,启发式搜索算法(即,应用正向逐步策略)。随后,通过6个经典模型和18名受试者评估了电极放置和特征贡献对凝视估计影响的比较分析。
结果:实验结果表明,在-50°至+50°的宽范围内,平均绝对误差(MAE)和均方根误差(RMSE)均取得了有希望的性能。MAE和RMSE最终可以提高到2.80°和3.74°,而只使用从2个通道提取的10个特征。与流行的基于EOG的技术相比,MAE和RMSE的性能改善范围从0.70°到5.48°和0.66°到5.42°,分别。
结论:我们通过系统地研究最佳通道/特征组合,提出了一种基于EOG的鲁棒凝视估计方法。实验结果不仅表明了所提出方法的优越性,而且表明了其临床应用的潜力。临床和翻译影响声明:准确的凝视估计是协助残疾和准确诊断包括ASD在内的各种疾病的关键步骤。帕金森病,和ADHD。所提出的方法可以通过EOG信号准确估计注视点,因此具有各种相关医疗应用的潜力。
Gaze estimation, as a technique that reflects individual attention, can be used for disability assistance and assisting physicians in diagnosing diseases such as autism spectrum disorder (ASD), Parkinson\'s disease, and attention deficit hyperactivity disorder (ADHD). Various techniques have been proposed for gaze estimation and achieved high resolution. Among these approaches, electrooculography (EOG)-based gaze estimation, as an economical and effective method, offers a promising solution for practical applications.
OBJECTIVE: In this paper, we systematically investigated the possible EOG electrode locations which are spatially distributed around the orbital cavity. Afterward, quantities of informative features to characterize physiological information of eye movement from the temporal-spectral domain are extracted from the seven differential channels.
METHODS: To select the optimum channels and relevant features, and eliminate irrelevant information, a heuristical search algorithm (i.e., forward stepwise strategy) is applied. Subsequently, a comparative analysis of the impacts of electrode placement and feature contributions on gaze estimation is evaluated via 6 classic models with 18 subjects.
RESULTS: Experimental results showed that the promising performance was achieved both in the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) within a wide gaze that ranges from -50° to +50°. The MAE and RMSE can be improved to 2.80° and 3.74° ultimately, while only using 10 features extracted from 2 channels. Compared with the prevailing EOG-based techniques, the performance improvement of MAE and RMSE range from 0.70° to 5.48° and 0.66° to 5.42°, respectively.
CONCLUSIONS: We proposed a robust EOG-based gaze estimation approach by systematically investigating the optimal channel/feature combination. The experimental results indicated not only the superiority of the proposed approach but also its potential for clinical application. Clinical and translational impact statement: Accurate gaze estimation is a key step for assisting disabilities and accurate diagnosis of various diseases including ASD, Parkinson\'s disease, and ADHD. The proposed approach can accurately estimate the points of gaze via EOG signals, and thus has the potential for various related medical applications.