Lie Detection: Truth Identification from EEG Signal Using Frequency and Time Features with SVM Classifier


  • Israa J. Mohammed Iraqi Commission for Computers & Informatics Informatics Institute for Postgraduate Studies
  • Dr. Loay E. George University of Information Technology and Communication.



EEG signal processing, Statistical Moments, Support vector machine


This study investigated the approach of extracting features from single EEG channels when the minimum number of features in Electroencephalogram (EEG) channels, hence the visibility of using sets of features extracted from a single channel. The feature sets considered in previous studies are utilized to establish a combined set of features extracted from one channel. The feature is the set of statistical moments. Publicly available EEG datasets like the Dryad dataset, obtained from 15 participants, are tested into a support vector machine classifier. The 12 channels were trained separately, where each channel was divided into a different number of blocks, and the results indicated that some channels were bad. Some were very encouraging, reaching 100% in the number of blocks 16 in channels 8 and 12. In this article, the comparison of ANN algorithm test results published in a previous article with SVM algorithm test results for the same tested features and channels will be presented.


S. Anwar, T. Batool, and M. Majid, “Event Related Potential (ERP) based Lie Detection using a Wearable EEG headset,” Proc. 2019 16th Int. Bhurban Conf. Appl. Sci. Technol. IBCAST 2019, pp. 543–547, 2019.

D. H. Y. Kulasinghe, “Faculty of information technology university of Moratuwa - Sri Lanka,” in Using EEG and Machine Learning to perform Lie Detection.”

Y. Xiong, L. Gu, and J. Gao, “Phase synchrony and its application to lie detection,” Proc. 2020 IEEE Int. Conf. Power, Intell. Comput. Syst. ICPICS 2020, pp. 726–729, 2020.

I. J. Mohammed and L. E. George, “Lie Detection and Truth Identification form EEG signals by using Frequency and Time Features,” J. Algebr. Stat, vol. 13, no. 3, pp. 4102–4121, 2022.

D. Dacunha-Castelle and M. Duflo Probability and Statistics, vol. II, 2012.

N. Zhou and L. Wang, “A modified T-test feature selection method and its application on the HapMap genotype data,” Genomics. Proteomics Bioinformatics, vol. 5, no. 3-4, pp. 242–249, 2007.

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,” Comput. Intell, vol. 36, no. 2, pp. 637–658, 2020.

B. Liu, “Uncertain risk analysis and uncertain reliability analysis,” J. Uncertain Syst, vol. 4, no. 3, pp. 163–170, 2010.

S. Dey, B. Al-Zahrani, and S. Basloom, “Dagum distribution: Properties and different methods of estimation,” Int. J. Stat. Probab, vol. 6, no. 2, pp. 74–92, 2017.

K. P. Balanda and H. L. Macgillivray Kurtosis : A Critical Review, vol. 42, pp. 111–119, 2012.

J. Cohen, “Statistical power analysis,” Curr. Dir. Psychol. Sci, vol. 1, no. 3, pp. 98–101, 1992.

I. E. Naqa and M. J. Murphy What is machine learning?,” in machine learning in radiation oncology, pp. 3–11, 2015. Springer

S. B. Maind and P. Wankar, “Research paper on basic of artificial neural network,” Int. J. Recent Innov. Trends Comput. Commun, vol. 2, no. 1, pp. 96–100, 2014.

I. Steinwart and A. Christmann, “Support vector machines,” 2008. Springer Science & Business Media.

A. Bablani, D. R. Edla, V. Kupilli, and R. Dharavath, “Lie Detection Using Fuzzy Ensemble Approach with Novel Defuzzification Method for Classification of EEG Signals,” IEEE Trans. Instrum. Meas, vol. 70, pp. 2021–2021.

Y. Kulasinghe and D. H. Y. Kulasinghe, “Using EEG and Machine Learning to perform Lie Detection Kinect sensor for skeleton abnormality detection View project Software Effort Estimation Model Based on Story Points for Agile Development View project Using EEG and Machine Learning to perform Lie D.”







How to Cite

Israa J. Mohammed and Dr. Loay E. George, “Lie Detection: Truth Identification from EEG Signal Using Frequency and Time Features with SVM Classifier”, WJCMS, vol. 1, no. 4, pp. 23–31, Dec. 2022, doi: 10.31185/wjcm.78.