Research on Eye Movement Classification Based on the Deep Learning Model

Abstract

To improve the recognition accuracy of eye movement direction based on EOGM, the Emotiv Epoc, and EEG acquisition headset was used to collect the EEG signals of the subjects in real time. After the 14-channel signals were filtered by wavelet, the short-time energy calculation method was used to select the two channels most sensitive to eye movement. The long and short-term memory network (LSTM) was then used to classify the eye movement signals. On the same data set, the experimental results show that the classification rates in the upper, lower, left, and right directions are up to 91%, 90%, 83%, and 75%, respectively, and the average classification rate is up to 84%. Compared with the existing classification methods, the classification accuracy of the proposed method is higher and the implementation process of the classification algorithm is simpler, which further verifies the feasibility and effectiveness of using EEG to identify the direction of eye movement.