ABSTRACT
ABSTRACT: A research on the relationship between eye movements and human behavior is a hot topic in the field of Human Activity Recognition (HAR). In this paper, a novel saccade signals detection algorithm for EOG-based HAR, which aims to improve the performance of HAR system, was proposed. In the proposed algorithm, Common Spatial Pattern (CSP) was utilized to build spatial filters, and then use it to process original multi-channel EOG signals. Consequently, feature parameters of different saccade signals can be acquired. To valid the performance of the proposed algorithm, a linear Support Vector Machine (SVM) was chosen. In lab environment, four types of saccade signals corresponding to up, down, left and right were used as analysis objects. Experimental results show that the accuracy recognition ratio is about 97.7%, which reveal that the proposed algorithm has a good classification performance in saccade signals analysis.
