National Academy Science Letters

, Volume 42, Issue 1, pp 33–37 | Cite as

A Real-Time Approach to Classify EEG Signals for Identifying Prevarication

  • Nandhini KesavanEmail author
  • Narasimhan Renga Raajan
Short Communication


Electroencephalography (EEG) is a recording method which captures brain action. In these frameworks, clients unequivocally control their brain action as opposed to utilizing motor movements to create signals that can be utilized to control computers or specialized gadgets. In this research, classifiers such as multilayer perceptron and bagging are utilized to quantify the exactness and accuracy of the acquired mind information. The percentage of recognition plays a major role as it indicates the person, the ratio he is in synch with viewing and thinking. EEG signal and P300 are used to measure the recordings done in the brain. On comparing the results of EEG and P300, it was found that recognition rate was good with the latter.


EEG MLP Bagging P300 Classification 


  1. 1.
    Subasi A, Ismail Gursoy M (2010) EEG signal classification using PCA, ICA, LDA and support vector machines. Expert Syst Appl 37:8659–8666CrossRefGoogle Scholar
  2. 2.
    Dubarry AS, Badier JM, Trébuchon-Da Fonseca A, Gavaret M, Carron R, Bartolomei F, Liégeois-Chauvel C, Régis J, Chauvel P, Alario FX, Bénar CG (2014) Simultaneous recording of MEG, EEG and intracerebral EEG during visual stimulation: from feasibility to single-trial analysis. NeuroImage 99:548–558CrossRefGoogle Scholar
  3. 3.
    Wang B, Wang X, Ikeda A, Nagamine T, Shibasaki H, Nakamura M (2014) Automatic reference selection for quantitative EEG interpretation: Identification of diffuse/localised activity and the active earlobe reference, iterative detection of the distribution of EEG rhythms. Med Eng Phys 36:88–95CrossRefGoogle Scholar
  4. 4.
    Fabbri-Destro M, Avanzini P, De Stefani E, Innocenti A, Campi C, Gentilucci M (2015) Interaction between words and symbolic gestures as revealed by N400. Brain Topogr 28:591–605CrossRefGoogle Scholar
  5. 5.
    Graimann B, Allison B, Pfurtscheller G (2010) Brain–computer interfaces: a gentle introduction, Brain–computer interfaces. The Frontiers Collection, Springer, LausanneCrossRefGoogle Scholar
  6. 6.
    Swords D, Sandygulova A, Abdalla S, O’Hare GMP (2013) Electroencephalograms for ubiquitous Robotic Systems. Proc Comput Sci 21:174–182CrossRefGoogle Scholar
  7. 7.
    Ubeyli ED (2009) Combined neural network model employing wavelet coefficients for EEG signals classification. Dig Sig Process 19:297–308CrossRefGoogle Scholar
  8. 8.
    Parvez MZ, Paul M (2014) Epilieptic seizure detection by analyzing EEG signals using different transformation techniques. Neurocomputing. 145:190–200CrossRefGoogle Scholar
  9. 9.
    Nandhini K, Santhi B (2012) Retrospection of SVM Classifier. J Theor Appl Inf Technol 38(1):83–88Google Scholar
  10. 10.
    Ding X, Li Y, Belatreche A, Maguire LP (2014) An experimental evaluation of novelty detection methods. Neurocomputing 135:313–327CrossRefGoogle Scholar
  11. 11.
    Lee J-H, Anaraki JR, Ahn CW, An J (2015) Efficient classification system based on fuzzy-rough feature selection and multitree genetic programing for intension pattern recognition using brain signal. Expert Syst Appl 42:1644–1651CrossRefGoogle Scholar
  12. 12.
    Muller K-R, Anderson CW, Birch GE (2003) Linear and nonlinear methods for brain–computer interfaces. IEEE Trans Neural Syst Rehabilit Eng 11(2):165–169CrossRefGoogle Scholar
  13. 13.
    Hendricks JC, Semwal SK (2014) EEG: the missing gap between controllers and gestures. In: Proceedings of the world congress on engineering and computer science, vol 1Google Scholar
  14. 14.
    Ahmed MA, Basori AH (2013) The influence of beta signal toward emotion classification for facial expression control through EEG sensors. Proc Soc Behav Sci 97:730–736CrossRefGoogle Scholar
  15. 15.
    Sankar SS, Rai R (2014) Human factors study on the usage of BCI headset for 3D CAD modeling. Comput Aided Des 54:51–55CrossRefGoogle Scholar
  16. 16.
    Agarwal SK, Shah S, Kumar R (2015) Classification of mental tasks from EEG data using backtracking search optimization based neural classifier. Neurocomputing 166:397–403CrossRefGoogle Scholar
  17. 17.
    Tan DS, Nijholt A (2010) Brain–computer interfaces applying our minds to human–computer interaction. Springer, New YorkGoogle Scholar
  18. 18.
    Kaufmann T, Herweg A, Kubler A (2014) Toward brain–computer interface based wheelchair control utilizing tactually-evoked event-related potentials. J Beuroeng Rehabilit 11:7Google Scholar
  19. 19.
    Yan W-J, Wang S-J, Liu Y-J, Wu Q, Fu X (2014) For micro-expression recognition: database and suggestions. Neurocomputing 136:82–87CrossRefGoogle Scholar
  20. 20.
    Gajic D, Djurovic Z, Gligorijevic J, Di Gennaro S, Savic-Gajic I (2015) Detection of epileptiform activity in EEG signals based on time–frequency and non-linear analysis. Front Comput Neurosci 9:38CrossRefGoogle Scholar
  21. 21.
    Ji H, Li J, Lu R, Gu R, Cao L, Gong X (2016) EEG classification for hybrid brain–computer interface using a tensor based multiclass multimodal analysis scheme. Comput Intell Neurosci 2016:15CrossRefGoogle Scholar

Copyright information

© The National Academy of Sciences, India 2018

Authors and Affiliations

  1. 1.SASTRA UniversityThanjavurIndia

Personalised recommendations