Identification of eye saccadic signatures in electrooculography data using time-series motifs

Abstract

This paper proposes a method for detecting eye saccadic signatures from Electrooculograms (EOG). Saccadic movements bear a particular pattern in the raw EOG time-series data. In this work, we interpret this signature of saccades as time-series motifs. The major issue in finding a saccade using standard motif identification methods is that saccadic signatures may be shrunk or stretched in the EOG time-series based on the saccadic duration. This shrinkage or stretching issue has been overcome in this work by using Dynamic Time Warping (DTW). This approach has been tested on our created EOG database and compared with existing methods of saccade detection in EOG. The proposed method shows higher accuracy as compared to existing ones. However, the execution time poses to be a limitation of the proposed method, which may be overcome by using high-speed processors and parallel computing platforms.