Cover
Vol. 22 No. 1 (2026)

Published: June 15, 2026

Pages: 1-9

Original Article

Denoising Techniques to Enhance P300 Signal Application of Lie Detection Technology Based-on EEG

Ali Rifaat Abd Almonim 1 Anas L. Mahmood 1
1 Electronic and Communications Engineering, Al-Nahrain University, Baghdad, Iraq
History
  • Received: July 31, 2023
  • Revised: September 8, 2023
  • Accepted: January 3, 2024
  • Available Online: June 15, 2026
DOI

https://doi.org/10.37917/ijeee.22.1.1

Abstract

In recent years, there has been a lot of interest in the study of P300 potential-based approaches for lie detection. The variations in brain signal activity (EEG-P300 component) that distinguish between lying and starting the truth are investigated. As soon as participants respond to an experiment stimulus for the first time, their brain signals are examined and the P300 signal is extracted. This paper aims to improve the signal-to-noise ratio (SNR) of P300, which leads to an increase in the classification accuracy of lie detection. Ten subjects were randomly assigned to groups of lying and innocent people, and 14 electrodes captured the EEG data for each group. This work proposed to use some denoising techniques like averaging the raw EEG signal, regression-based baseline correction, and independent component analysis (ICA). The suggested approach and other early published methods vary mostly in the regression-based technique used in bassline correction to adaptively indicate the baseline interval (baseline window). Compared to other studies, the suggested technique gives an increase in the mean amount of SNR by up to 20% was obtained.

Keywords

References

  1. P. Wolpe, K. Foster, and D. LangLeben, “Emerging neurotechnology for lie-detection: Promises and perils,” The American Journal of Bioethics, vol. 8, no. 2, pp. 520– 531, 2001.
  2. A. Bablani, D. Reddy, and V. Kuppili, “Deceit identification test on eeg data using deep belief network,” in IEEE International Conference on Computing and Networking Technology, pp. 1–4, 2018.
  3. M. Affan, J. Can, and K. George, “Deep learning applications in brain-computer interface based lie detection,” in IEEE 13th Annual Computing and Communication Workshop and Conference, pp. 189–192, 2023.
  4. S. Ji and et al, “Suicidal ideation detection: a review of machine learning methods and applications,” IEEE Transactions on Computational Social Systems, vol. 8, no. 1, pp. 214–226, 2021.
  5. Z. Hu, Y. Xia, and J. Xiong, “Lie detection based on a difcw radar with machine learning,” in IEEE MTT-S International Wireless Symposium, pp. 1–3, 2021.
  6. N. Baghel, D. Singh, M. Kishore, R. Burget, and V. Myska, “Truth identification from eeg signal by using convolution neural network: Lie detection,” in IEEE International Conference on Telecommunications and Signal Processing, pp. 550–553, 2020.
  7. J. P. Rosenfeld, “P300 in detecting concealed information and deception: A review,” Psychophysiology, vol. 57, 2020.
  8. J. P. Rosenfeld and et al, “The complex trial protocol (ctp): A new, countermeasure-resistant, accurate, p300- based method for detection of concealed information,” Psychophysiology, vol. 45, pp. 906–919, 2008.
  9. S. Dodia, D. R. Edla, A. Bablani, and R. Cheruku, “Lie detection using extreme learning machine: A concealed information test based on short-time fourier transform and binary bat optimization using a novel fitness function,” Computational Intelligence, vol. 36, pp. 637–658, 2019.
  10. Satyender, S. Kumar, and K. Kant, “Eeg artifact removal using canonical correlation analysis and emd-dfa based hybrid denoising approach,” Procedia Computer Science, vol. 218, pp. 2081–2090, 2023.
  11. P. Alday, “How much baseline correction do we need in erp research? extended glm model can replace baseline correction while lifting its limits,” Psychophysiology, vol. 56, p. e13451, 2019.
  12. K. Kotowski, J. Ochab, K. Stapor, andW. Sommer, “The importance of ocular artifact removal in single-trial erp analysis: The case of the n250 in face learning,” Biomedical Signal Processing and Control, vol. 79, 2023.
  13. P. Ablin, J. Cardoso, and A. Gramfort, “Faster independent component analysis by preconditioning with hessian approximations,” IEEE Transactions on Signal Processing, vol. 66, no. 15, pp. 4040–4049, 2018.
  14. A. Al-Saegh, “Comparison of complex-valued independent component analysis algorithms for eeg data,” Iraqi Journal for Electrical and Electronic Engineering, vol. 15, 2019.
  15. G. Czanner and et al, “Measuring the signal-to-noise ratio of a neuron,” Proceedings of the National Academy of Sciences, vol. 112, pp. 7141–7146, 2015.
  16. A. Gonzalez-Moreno and et al, “Signal-to-noise ratio of the meg signal after preprocessing,” Journal of Neuroscience Methods, vol. 222, pp. 56–61, 2014.
  17. Q. Kang and et al, “Exploring the functional brain network of deception in source-level eeg via partial mutual information,” Electronics, vol. 12, 2023.
  18. R. Mckearney, S. Bell, M. Chesnaye, and D. Simpson, “Optimizing weighted averaging for auditory brainstem response detection,” Biomedical Signal Processing and Control, vol. 83, 2023.
  19. Larson and et al, “Mne-python (v1.3.1).” https:// zenodo.org/record/2023, 2023. Zenodo.
  20. N. Trusbak, L. Parkkonen, M. Kliuchko, P. Vuust, and E. Brattico, “Comparing the performance of popular meg/eeg artifact correction methods in an evokedresponse study,” Computational Intelligence and Neuroscience, vol. 2016, 2016.
  21. Y. Wan, Y. Zhou, and Z. Yang, “Blind source separation of fast ica algorithm based on negentropy and its optimization,” in Proceedings of SPIE, vol. 12597, 2023.
  22. A. Li and et al, “Mne-ica label: Automatically annotating ica components with ic label in python,” The Journal of Open Source Software, vol. 4484, 2022.
  23. I. A. Imran and M. Rabbani, “Comparison of deep learning & adaptive algorithm performance for de-noising eeg,” Journal of Physics: Conference Series, vol. 2325, 2022.
  24. J. Yu, C. Li, K. Lou, C. Wei, and Q. Liu, “Embedding decomposition for artifacts removal in eeg signals,” Journal of Neural Engineering, vol. 19, 2022.
  25. J. Polich, “Neuropsychology of p300,” in The Oxford Handbook of Event-Related Potential Components, pp. 159–188, 2012.