In recent years, with the development of brain science and biomedical engineering, as well as the rapid development of electroencephalogram (EEG) signal analysis methods, using EEG signals to monitor human health has become a very popular research field. The innovation of this paper is to analyze the EEG signal for the first time by building a depth factorization machine model, so that on the basis of analyzing the characteristics of user interaction, we can use EEG data to predict the binomial state of eyes (open eyes and closed eyes). The significance of the research is that we can diagnose the fatigue and the health of the human body by detecting the state of eyes for a long time. On the basis of this inference, the proposed method can make a further useful auxiliary support for improving the accuracy of the recommendation system recommendation results. In this paper, we first extract the features of EEG data by wavelet transform technology and then build a depth factorization machine model (FM+LSTM) which combines factorization machine (FM) and Long Short-Term Memory (LSTM) in parallel. Through the test of real data set, the proposed model gets more efficient prediction results than other classifier models. In addition, the model proposed in this paper is suitable not only for the determination of eye features but also for the acquisition of interactive features (user fatigue) in the recommendation system. The conclusion obtained in this paper will be an important factor in the determination of user preferences in the recommendation system, which will be used in the analysis of interactive features by the graph neural network in the future work.

中文翻译：

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基于脑电信号和特征交互建模的眼睛行为预测研究。

近年来，随着脑科学和生物医学工程的发展，以及脑电图（EEG）信号分析方法的迅速发展，使用EEG信号监测人体健康已成为非常受欢迎的研究领域。本文的创新之处在于，通过构建深度分解机模型来首次分析脑电信号，以便在分析用户交互特征的基础上，我们可以使用脑电数据预测眼睛的二项式状态（开放眼睛和闭着眼睛）。该研究的意义在于，通过长时间检测眼睛的状态，我们可以诊断出人体的疲劳和健康状况。根据这个推论，所提出的方法可以为提高推荐系统推荐结果的准确性提供进一步有用的辅助支持。本文首先利用小波变换技术提取脑电数据的特征，然后建立并行结合了分解机（FM）和长短期记忆（LSTM）的深度分解机模型（FM + LSTM）。通过对真实数据集的测试，提出的模型比其他分类器模型获得更有效的预测结果。此外，本文提出的模型不仅适用于确定眼部特征，还适用于推荐系统中交互特征（用户疲劳）的获取。本文得出的结论将是确定推荐系统中用户偏好的重要因素，