Cognitive Activity Recognition Based on Electrooculogram Analysis

  • Anwesha Banerjee
  • Shreyasi Datta
  • Amit Konar
  • D. N. Tibarewala
  • Janarthanan Ramadoss
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 27)


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.


Activity Recognition Electrooculogram Support Vector Machine 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Anwesha Banerjee
    • 1
  • Shreyasi Datta
    • 2
  • Amit Konar
    • 2
  • D. N. Tibarewala
    • 1
  • Janarthanan Ramadoss
    • 3
  1. 1.School of Bioscience & EngineeringJadavpur UniversityJadavpurIndia
  2. 2.Department of Electronics & Telecommunication EngineeringJadavpur UniversityJadavpurIndia
  3. 3.Department of Computer ScienceTJS Engineering CollegeGummidipoondiIndia

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