Efficient Classification of EOG using CBFS Feature Selection Algorithm
نویسندگان
چکیده
This work select the features in high dimensional data using eye movements of reading and writing by ElectroOculoGraph (EOG) signals. EOG measures the changes in the electric potential field caused by eye movements. This work has three phases; the first phase identifies and removes noise from the signal. The second phase involves analysis of EOG signals by CBFS Feature Selection method and the third phase classifies EOG signals using SMO, a SVM based classifier.
منابع مشابه
The Role of Feature Selection with Applications to Eye Movements using Electrooculography
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 signa...
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