A new EOG-based eyeblink detection algorithm

Abstract

Accurate and efficient operator functional state classification and assessment based on physiological data have many important applications ranging from operator monitoring to interaction and control of human/machine systems. Eyeblink characteristics are frequently used as physiological indicators for this purpose. In this paper, we describe an efficient and robust eyeblink detection algorithm based on nonlinear analysis of the electrooculogram (EOG) signal. The performance of the algorithm was evaluated via data analysis results of several benchmark test sets in comparison with another eyeblink detection algorithm. Copyright 1998 Psychonomic Society, Inc.