A New Multi-resolution Analysis Method for Electrooculography Signals

Electrooculography (EOG) signals indicate the degree and direction of eye movements. Hence, EOG signals have been useful in eye movement controlled rehabilitation systems. Denoising and accurate identification of the type of eye movement in EOG signals are the major challenges in their analysis. The state-of-the-art techniques for EOG signal analysis concerning denoising and eye movement extraction are based on multi-resolution analysis using wavelet bases, such as Haar or Daubechies. However, these wavelets are designed for general purpose signal processing applications and hence are not optimized for the EOG signal structures. In this paper, we propose a new multi-resolution basis specific to the analysis of EOG signals. The scaling and wavelet functions for the basis are derived from the signatures of blinks and saccades respectively, and hence we name them as blinklets and saclets accordingly, thereby forming a new multi-resolution basis. These descriptors are found to be more effective than standard wavelets for EOG signals, signal denoising, and for identifying the different eye movement signatures such as saccades, blinks, smooth pursuits, and fixations, as tested on the Physiosig and Centre for Biomedical Cybernetics Eye Movement (CBC-EM) EOG Databases.