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An Efficient Deep Learning Paradigm for Deceit Identification Test on EEG Signals

Published: 03 June 2021 Publication History

Abstract

Brain-Computer Interface is the collaboration of the human brain and a device that controls the actions of a human using brain signals. Applications of brain-computer interface vary from the field of entertainment to medical. In this article, a novel Deceit Identification Test is proposed based on the Electroencephalogram signals to identify and analyze the human behavior. Deceit identification test is based on P300 signals, which have a positive peak from 300 ms to 1,000 ms of the stimulus onset. The aim of the experiment is to identify and classify P300 signals with good classification accuracy. For preprocessing, a band-pass filter is used to eliminate the artifacts. The feature extraction is carried out using “symlet” Wavelet Packet Transform (WPT). Deep Neural Network (DNN) with two autoencoders having 10 hidden layers each is applied as the classifier. A novel experiment is conducted for the collection of EEG data from the subjects. EEG signals of 30 subjects (15 guilty and 15 innocent) are recorded and analyzed during the experiment. BrainVision recorder and analyzer are used for recording and analyzing EEG signals. The model is trained for 90% of the dataset and tested for 10% of the dataset and accuracy of 95% is obtained.

References

[1]
Rabie A. Ramadan, S. Refat, Marwa A. Elshahed, and Rasha A. Ali. 2015. Basics of brain computer interface. Brain-Computer Interfaces. Springer, Cham, 31–50.
[2]
Amjed S. Al-Fahoum, and Ausilah A. Al-Fraihat. 2014. Methods of EEG signal features extraction using linear analysis in frequency and time-frequency domains. ISRN Neurosci. 13 (2014), 720218.
[3]
Rabie A. Ramadan, and Athanasios V. Vasilakos. 2017. Brain computer interface: control signals review. Neurocomputing 223 (2017), 26–44.
[4]
A. Luiz S. Ferreira, Leonardo Cunha de Miranda, E. Esteves C. de Miranda, and Sarah Gomes Sakamoto. 2013. A survey of interactive systems based on brain-computer interfaces. SBC J. Interact. Syst. 4, 1 (2013), 3–13.
[5]
J. Satheesh Kumar and P. Bhuvaneswari. 2012. Analysis of Electroencephalography (EEG) signals and its categorization–A study. Procedia Eng. 38 (2012), 2525–2536.
[6]
Sang Han Choi, Minho Lee, Yijun Wang, and Bo Hong. 2006. Estimation of optimal location of EEG reference electrode for motor imagery-based BCI using fMRI. In Proceedings of the 28th International Conference of the IEEE Engineering in Medicine and Biology Society.
[7]
Don Grubin. 2010. The polygraph and forensic psychiatry. Journal of the American Academy of Psychiatry and the Law 38, 4 (2010), 446--451.
[8]
Terence W. Picton. 1992. The P300 wave of the human event-related potential. J. Clin. Neurophys. 9, 4 (1992), 456–479.
[9]
Andrew B. Schwartz. 2004. Cortical neural prosthetics. Ann. Rev. Neurosci. 27 (2004), 487–507.
[10]
John B. Meixner and Rosenfeld J. Peter. 2011. A mock terrorism application of the P300‐based concealed information test. Psychophysiology 48, 2 (2011), 149–154.
[11]
Syed Kamran Haider, Malik Imran Daud, Aimin Jiang, and Zubair Khan. 2017. Evaluation of P300 based Lie Detection Algorithm. Electric. Electron. Eng. 7, 3 (2017), 69–76.
[12]
Kusuma Mohanchandra. 2015. Criminal forensic: An application to EEG. In Proceedings of the Conference on Recent and Emerging trends in Computer and Computational Sciences (RETCOMP’15).
[13]
Ying-Fang Lai, Mu-Yen Chen, and Hsiu-Sen Chiang. 2018. Constructing the lie detection system with fuzzy reasoning approach. Granul. Comput. 3 (2018), 169--176. https://doi.org/10.1007/s41066-017-0064-3
[14]
Adhemar Bultheel. 1995. Learning to swim in a sea of wavelets. Bull. Belgian Math. Soc. 2, 1 (1995), 1–45.
[15]
Yoshua Bengio. 2009. Learning deep architectures for AI. Found. Trends Mach. Learn. 2, 1 (2009), 1–127.
[16]
Brain products. 2017. Retrieved from: http://www.brainproducts.com/.
[17]
Easycap. 2017. Retrieved from: http://www.easycap.de/e/products/products.htm15.
[18]
Patrick Celka, B. Whitcher, P. F. Craigmile, P. Brown, A. Hegde, D. Erdogmus, D. S. Shiau, J. C. Principe, C. J. Sackellares, F. Amor, et. al. 2005. Special issue on neuronal coordination in the brain: A signal processing perspective. Sig. Proc. 85 (2005), 11.
[19]
Fabien Lotte. 2014. A tutorial on EEG signal-processing techniques for mental-state recognition in brain-computer interfaces. Guide to Brain-computer Music Interfacing. Springer, London, 133–161.
[20]
Abdulhamit Subasi. 2007. EEG signal classification using wavelet feature extraction and a mixture of expert model. Exp. Syst. Applic. 32, 4 (2007), 1084–1093.
[21]
Noor Kamal Al-Qazzaz, Sawal Ali, SitiAnom Ahmad, MdShabiul Islam, and MohdIzhar Ariff. 2014. Selection of mother wavelets thresholding methods in denoising multi-channel EEG signals during working memory task. In Proceedings of the IEEE Conference on Biomedical Engineering and Sciences (IECBES’14).
[22]
Hojjat Adeli, Ziqin Zhou, and Nahid Dadmehr. 2003. Analysis of EEG records in an epileptic patient using wavelet transform. J. Neurosci. Meth. 123, 1 (2003), 69–87.
[23]
Vahid Abootalebi, Mohammad Hassan Moradi, and Mohammad Ali Khalilzadeh. 2009. A new approach for EEG feature extraction in P300-based lie detection. Comput. Meth. Prog. Biom. 94, 1 (2009), 48–57.
[24]
Manisha Chandani and Arun Kumar. 2017. Classification of EEG physiological signal for the detection of epileptic seizure by using DWT feature extraction and neural network. Amer. J. Inf. Manag. 2, 3 (2017), 37–42.
[25]
Ibrahim Omerhodzic, Samir Avdakovic, Amir Nuhanovic, and Kemal Dizdarevic. 2013. Energy distribution of EEG signals: EEG signal wavelet-neural network classifier. arXiv preprint arXiv:1307.7897 (2013).
[26]
Bruno A. Olshausen and David J. Field. 1996. Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381, 6583 (1996), 607.
[27]
Elias Ebrahimzadeh, Seyed Mohammad Alavi, Ahmad Bijar, and Alireza Pakkhesal. 2013. A novel approach for detection of deception using Smoothed Pseudo Wigner-Ville Distribution (SPWVD). J. Biomed. Sci. Eng. 6, 1 (2013), 8.
[28]
Junfeng Gao, Hongjun Tian, Yong Yang, Xiaolin Yu, Chenhong Li, and Nini Rao. 2014. A novel algorithm to enhance P300 in single trials: Application to lie detection using F-score and SVM. PLoS One 9, 11 (2014), e109700.
[29]
J. Peter Rosenfeld et al. 2004. Simple, effective countermeasures to P300-based tests of detection of concealed information. Psychophysiology 41, 2 (2004), 205--219.
[30]
Y.-F. Lai, M.-Y. Chen, and H.-S. Chiang. 2018. Constructing the lie detection system with fuzzy reasoning approach. Granul. Comput. 3 (2018), 169--176. https://doi.org/10.1007/s41066-017-0064-3
[31]
M. Zhao, C. Zhao, and C. Zheng. 2011. Identifying concealed information using wavelet feature extraction and support vector machine. Procedia Environ. Sci. 8 (2011), 337–343.
[32]
J. P. Rosenfeld, E. Labkovsky, M. Winograd, M. A. Lui, C. Vandenboom, and E. Chedid. 2008. The complex trial protocol (CTP): A new, countermeasure‐resistant, accurate, P300-based method for detection of concealed information. Psychophysiology 45, 6 (2008), 906–919.
[33]
Matthias Kaper, Peter Meinicke, Ulf Grossekathoefer, Thomas Lingner, and Helge Ritter. 2004. BCI competition 2003-dataset IIb: Support vector machines for the P300 speller paradigm. IEEE Trans. Biomed. Eng. 51 (2004), 1073–1076
[34]
Lawrence A. Farwell and Emanuel Donchin. 1991. The truth will out: Interrogative polygraphy (“lie detection”) with event-related brain potentials. Psychophysiology 28 (1991), 531–547.
[35]
J. Peter Rosenfeld, Matthew Soskins, Gregory Bosh, and Andrew Ryan. 2004. Simple, effective countermeasures to P300-based tests of detection of concealed information. Psychophysiology 41, 2 (2004), 205--219.
[36]
Albert Cohen, Ingrid Daubechies, and J.-C. Feauveau. 1992. Biorthogonal bases of compactly supported wavelets. Commun. Pure Appl. Math. 45, 5 (1992), 485–560.
[37]
Kenta Kubo and Hiroshi Nittono. 2009. The role of intention to conceal in the P300-based concealed information test. Appl. Psychophys. Biofeed. 34 (2009), 227–235. https://doi.org/10.1007/s10484-009-9089-y
[38]
W. Chang, H. Wang, C. Hua, Q. Wang, and Y. Yuan. 2019. Comparison of different functional connectives based on EEG during concealed information test. Biomed. Sig. Proc. Contr. 49 (2019), 149–159.
[39]
Syed Anwar, Tahira Batool, and Muhammad Majid. 2019. Event Related Potential (ERP) based Lie Detection using a Wearable EEG headset. In Proceedings of the 16th International Bhurban Conference on Applied Sciences and Technology (IBCAST’19). IEEE, 543–547.
[40]
Shubham Dodia, Damodar R. Edla, Annushree Bablani, and Ramalingaswamy Cheruku. 2020. 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. 36, 2 (2020), 637--658.
[41]
H. Adeli, Z. Zhou, and N. Dadmehr. 2003. Analysis of EEG records in an Epileptic patient using wavelet transform. J. Neurosci. Meth. 123 (2003), 69–87.
[42]
I. Daubechies. 1990. The wavelet transforms, timefrequency localization and signal analysis. IEEE Trans. Inf. Theor. 36, 5 (1990), 529–531.
[43]
D. E. Rumelhart, G. E. Hinton, and R. J. Williams. 1985. Learning Internal Representations by Error Propagation. Technical Report. University of California, San Diego. La Jolla Institute for Cognitive Science.
[44]
G. E. Hinton and R. R. Salakhutdinov. 2006. Reducing the dimensionality of data with neural networks. Science 313 (2006), 5786, 504–507.
[45]
L. Theis, W. Shi, A. Cunningham, and F. Huszár. 2017. Lossy image compression with compressive autoencoders. arXiv preprint arXiv:1703.00395 (2017).
[46]
P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P.-A. Manzagol. 2010. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11 (Dec. 2010), 3371–3408.
[47]
Abhijith V. Nair, Murali Kumar Kodidasu, and Mathew Jimson. 2018. An improved approach for EEG signal classification using autoencoder. In Proceedings of the 8th IEEE International Symposium on Embedded Computing and System Design (ISED’18).
[48]
Yoshua Bengio, Pascal Lamblin, Dan Popovici, and Hugo Larochelle. 2007. Greedy layer-wise training of deep networks. In Proceedings of the International Conference on Advances in Neural Information Processing Systems.

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    Published In

    cover image ACM Transactions on Management Information Systems
    ACM Transactions on Management Information Systems  Volume 12, Issue 3
    September 2021
    225 pages
    ISSN:2158-656X
    EISSN:2158-6578
    DOI:10.1145/3468067
    Issue’s Table of Contents
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Publication History

    Published: 03 June 2021
    Accepted: 01 March 2021
    Revised: 01 February 2021
    Received: 01 May 2019
    Published in TMIS Volume 12, Issue 3

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    Author Tags

    1. Brain-computer interface
    2. electroencephalogram
    3. deceit identification test
    4. wavelet packet transform
    5. deep neural network

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    • (2024)EEG and fNIRS Signal-Based Emotion Identification by Means of Machine Learning Algorithms During Visual Stimuli ExposureElectronics10.3390/electronics1323479713:23(4797)Online publication date: 5-Dec-2024
    • (2024)Supervised Learning Approaches for Deceit Identification: Exploring EEG as a Non-invasive TechniqueCryptology and Network Security with Machine Learning10.1007/978-981-97-0641-9_12(179-190)Online publication date: 23-Apr-2024
    • (2024)Identifying the Risk in Lie Detection for Assessing Guilty and Innocent Subjects for Healthcare ApplicationsHealthcare Industry Assessment: Analyzing Risks, Security, and Reliability10.1007/978-3-031-65434-3_2(25-41)Online publication date: 3-Aug-2024
    • (2023)LSTMNCP: lie detection from EEG signals with novel hybrid deep learning methodMultimedia Tools and Applications10.1007/s11042-023-16847-z83:11(31655-31671)Online publication date: 18-Sep-2023
    • (2023)RETRACTED ARTICLE: PSO-based optimization for EEG data and SVM for efficient deceit identificationSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-023-08476-327:14(9835-9843)Online publication date: 29-May-2023
    • (2023)Electroencephalographic Signal Processing from Brain-Computer-Interface Following Image-Based Emotion InductionAmbient Intelligence—Software and Applications—13th International Symposium on Ambient Intelligence10.1007/978-3-031-22356-3_23(239-248)Online publication date: 1-Jan-2023
    • (2022)Emotion Classification from EEG with a Low-Cost BCI Versus a High-End EquipmentInternational Journal of Neural Systems10.1142/S012906572250041132:10Online publication date: 25-Jul-2022
    • (2022)Learning Brain Computer Interface model for Amyotrophic Lateral Sclerosis (ALS) people using Random Forest – Linear Discriminant Analysis (RF-LDA) Algorithm2022 IEEE 19th India Council International Conference (INDICON)10.1109/INDICON56171.2022.10039916(1-10)Online publication date: 24-Nov-2022

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