A Comparative Experimental Study Between Instantaneous and Convolutional BSS Models for Saccadic EOG Signal Separation

Abstract

Saccadic eye movement as a voluntary activity has become the basis of a promising approach for the implementation of human-computer interaction. Seeking blind source separation (BSS) model that can exactly describe eye movement generation and improve the quality of saccadic electrooculography (EOG) signals, we performed a comparative study between an instantaneous and a convolutional mixing model for multichannel EOG signals. Experiments involving seven subjects were carried out in a laboratory environment. The independent component analysis (ICA) method was adopted for BSS. The statistical indicators of the residual statistical dependence and the total square cross correlation for the convolutional model were 0.04 (second order)/0.08 (fourth order) and 1.25, respectively, which were lower than those of the instantaneous model by 0.3 (second order)/0.17 (fourth order) and 0.32, respectively. In addition, the average classification accuracies of the convolutional model were 95.76% (within subject) and 94.08% (between subject) in the case of upward, downward, left, and right saccade tasks, which were higher than those of the instantaneous model by 1.69% and 5.53%, respectively. Experiments revealed that the quality of the saccadic signals separated using the convolutional model was higher than that achieved using the instantaneous model. The outcomes of this research can serve as a valuable reference for multichannel EOG analysis and application.