The Role of Feature Selection with Applications to Eye Movements using Electrooculography
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Eyes are the windows to the brain and the eye movements are a rich source of information in information processing. The aim of this paper is to select the features with CBFS Feature selection algorithm using eye movements by ElectroOculoGraph (EOG) signals during reading and writing task. The objective is to impart the fundamental functionality to get an extensive understanding of how EOG signals can be applied in human computer interaction (HCI) and what can be inferred from those signals using feature selection and data mining classification techniques. This paper first identifies the importance of eye movements and EOG signals then analyze EOG signals by CBFS (clearness based feature selection), mRMR (minimum redundancy maximum relevance) feature Selection methods and the third section analyzes the time complexity of CBFS method and describes the performance of data mining classification in EOG signals.
KEYWORDS:Eye Movements; Feature Selection; CBFS; mRMR; Classification
