Cognitive Activity Recognition Based on Electrooculogram Analysis
Conference paper
Abstract
This work is aimed at identification of human cognitive activities from the analysis of their eye movements using Electrooculogram signals. These signals are represented through Adaptive Autoregressive Parameters, Wavelet Coefficients, Power Spectral Density and Hjorth Parameters as signal features. To distinctly identify a particular class of cognitive activity, the obtained feature spaces are classified using Support Vector Machine with Radial Basis Function Kernel. An average accuracy of 90.39% for recognition of eight types of cognitive activities has been achieved in a one-versus-all classification approach.
Keywords
Activity Recognition Electrooculogram Support Vector MachinePreview
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