Feature extraction and classification method of electrooculogram based on variational modal decomposition
Xingyu Jing, Zhiyuan Jing · 2022
With the progress of science and technology, human-computer interaction technology has become a research hotspot. Many new interaction methods have been developed. As an important organ to receive information, eye plays an important role in human-computer interaction. In this paper, an electrooculogram acquisition system is designed by using five electrodes to measure the amplitude of electrooculogram. The 3Hz and 8Hz components of electrooculogram are extracted by variational modal decomposition. The maximum, minimum, distance between maximum and minimum, mean and standard deviation of these frequency components are used as features. KNN, SVM, CART and Bagging classifiers are used to classify electrooculogram. In the two-dimensional coordinate system, the highest accuracy of the classification of 44 eye rotation angles is 88.40%, which is 12.50% higher than the accuracy of only using the time domain features. It proves the effectiveness of the proposed method and provides a richer interaction mode for human-computer interaction.