E.O.G classification based on fuzzified symbolic representation

Article type: Research Article

Authors: Triki, Slim | Kamoun, Anas

Affiliations: Research Unit on Renewable Energies and Electric Vehicles (RELEV), University of Sfax, Sfax Engineering School (ENIS), Tunisia

Note: [] Corresponding author. Slim Triki, Research Unit on Renewable Energies and Electric Vehicles (RELEV), University of Sfax, Sfax Engineering School (ENIS), Tunisia. E-mail: slim.triki1@gmail.com

Abstract: This work is part of a study for a 3D navigation task of disabled person in virtual environment. The hardware used is an ocular command device called Cyclope which is based on the measurement and analyze of biological signals that vary with eye movements (Electro Oculo-Graphic, EOG). This paper presents a method to represent and classify EOG signal. The goal is to distinguish Ocular Command Movement which can be detected from pattern of EOG signals. Then, an adapted representation and classification of these patterns allows identifying a particular class of OCM. The proposed method extracts semi-qualitative linear episodes from biological signals. Two membership functions are implemented to transform the quantitative information in the linear episodes to a purely symbolic representation. Finally, the symbolic data is classified with qualitative similarity index.

Keywords: Electro-oculography (EOG), fuzzy trend analysis, classification, pattern matching

DOI: 10.3233/IFS-130951

Journal: Journal of Intelligent & Fuzzy Systems, vol. 26, no. 6, pp. 2841-2851, 2014