Identifying brain abnormalities from electroencephalogram using evolutionary gravitational neocognitron neural network

  • P. GomathiEmail author
  • S. Baskar
  • P. Mohamed Shakeel
  • V. R. Sarma Dhulipala


Now-a-day’s brain abnormality is one among the dangerous neurological disorders that occurs because of the birth defects, brain stroke, brain injuries, genetic mutation and brain tumor. This brain disorder creates continue melancholia, bipolar disorder, stress disorder (PTSD) and so on. Due to this serious impact of the brain abnormalities, need to be identified within the beginning stage for eliminating the difficulties in humans day to day life. So, the automatic brain abnormality prediction process is created by utilizing electroencephalogram (EEG) for avoiding risk factor in future. As per the discussion, this paper introduces the evolutionary gravitational Neocognitron neural network(GNNN) for recognizing brain abnormalities with effective manner and it is especially suited for humans in war field. Initially, EEG signal is collected from patient; unwanted signal information is eliminated by using multi-linear principal component analysis from pre-processed signal, various features are extracted using affine invariant component analysis method and greedy global optimized features are chosen. The chosen features are analyzed using multi-layer virtual cortex model for predicting abnormal features. Finally the potency of the brain related abnormality prediction process developed using MATLAB tool and efficiency is examined using F-measure, Mathew correlation coefficient error rate, sensitivity, specificity, and accuracy. Along these lines the proposed framework effectively perceives the cerebrum variation from the norm with most astounding precision up to 99.48% with error rate.


Brain abnormality Electroencephalogram (EEG) Multi-linear principal component Affine invariant component analysis Multi-layer virtual cortex Mathew correlation coefficient and accuracy 



The authors would like to thank SERB, Science and Engineering Research Board, New Delhi, India for the funding to carry out the research work from N.S.N College of Engineering and Technology, Karur, Tamil Nadu,India.

Compliance with ethical standards

Conflicts of interest



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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • P. Gomathi
    • 1
    Email author
  • S. Baskar
    • 2
  • P. Mohamed Shakeel
    • 3
  • V. R. Sarma Dhulipala
    • 4
  1. 1.Department of Electrical and Electronics EngineeringN.S.N. College of Engineering and TechnologyKarurIndia
  2. 2.Department of Electronics and Communication Engineering/Centre for Interdisciplinary ResearchKarpagam Academy of Higher EducationCoimbatoreIndia
  3. 3.Faculty of Information and Communication TechnologyUniversiti Teknikal MalaysiaMelakaMalaysia
  4. 4.Department of PhysicsAnna University, BIT-CampusTiruchirappalliIndia

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