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Classification of electrooculograph signals: Comparing conventional classifiers using CBFS feature selection algorithm

This work select the features in high dimensional data using CBFS Feature selection algorithm by ElectroOculoGraph (EOG) signals using eye movements of reading and writing task. 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 various conventional classifiers.