EEG Signal and Feature Interaction Modeling-Based Eye Behavior Prediction Research.

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