Deep Learning based Attribute Identification for Deceit Prediction Using EEG Signal Analysis

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Published: Apr 30, 2025
Keywords:
Deceit detection EEG signal analysis Sample entropy(SampEn) Recurrence quantification analysis (RQA) Long short-term memory (LSTM) Brainwave patterns Neural classification Machine learning Time- series analysis

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Revoori Swetha*
Department of Computer Science and Engineering, National Institute of Technology, Goa, Farmagudi, Ponda, Goa- 403401,India
Dr. Damodar Reddy Edla
Department of Computer Science and Engineering, National Institute of Technology, Goa, Farmagudi, Ponda, Goa-403401, India

Abstract

Lying detection is in the spotlight these days, particularly when it is applied to brain activity scanning. It is so because it may prove to be revolutionary for psychology, security, and even law enforcement agencies. It is one of the new techniques based on the detection of lies through examination of brainwave patterns derived from EEG signals using some very high-end techniques. Particularly, the approach applies Sample Entropy and Recurrence Quantification Analysis to give cues to Long Short-Term Memory networks. The belief is SampEn and RQA can pull out the non-linear, dynamic, and complex features of EEG data that are believed to be consistent triggers to indicate that someone is lying. To simplify the data, Independent Component Analysis simplifies the number of EEG channels from 16 to 12. To monitor the performance of the model, the researchers employed indicative metrics such as ROC curves, F1 score, precision, and recall. Trained on feature values based on SampEn and RQA, the LSTM model had an accuracy of 97.66%. To avoid having the model overfit the training data, training was stopped early at epoch 81. Its performance was also benchmarked against more sophisticated neural networks, such as Multi-Layer Spiking Neural Networks, and conventional signal processing techniques, such as Fourier transform and wavelet analysis. The findings indicate that the algorithm delivers a perfect balance between high accuracy and computational efficiency, and therefore is best suited for real-world applications of lie detection. Detection of deception from EEG activity is an artificial intelligence breakthrough and a cognitive neuroscience milestone. Compared to all the other lie detection techniques, such as the polygraph which search for body reaction possibly under duress, EEG machines record an initial indicator of brain activity. Sample Entropy and Recurrence Quantification Analysis sophisticated signal processing techniques are employed to navigate through the very intricate EEG signal. Canceling repeat noise and detecting critical information characteristics, such as algorithms can detect subtle trends of falsehoods

Article Details

Issue
Vol. 167 No. A2 (S) (2025): Technologies and Their Effects on Real-Time Social Development
Section
Articles

References

Akhavan A, Moradi MH, and Vand SR Subject- based discriminative sparse representation model for detection of concealed information Comput Methods Programs Biomed 2017 143 25-33.

Bablani A, Edla DR, and Tripathi D An efficient concealed information test: EEG feature extraction and ensemble classification for lie identification Mach Vis Appl 2019 30 5 813-832.

Gao J, Lu L, Yang Y, Yu G, Na L, and Rao N A novel concealed information test method based on independent component analysis and support vector machine ClinEEGNeurosci20124354-63.

Sung M, Kim Y (2020) Training spiking neural networkswithanadaptiveleakyintegrate-and-fire neuron.In:2020IEEEinternationalconferenceon consumer electronics-Asia (ICCE-Asia), pp. 1–2, IEEE.

Farwell LA and Donchin E The truth will out: interrogative polygraphy (‘lie detection’) with event-related brain Potentials Psychophysiology 1991285531-547.

Arasteh A, Moradi MH, and Janghorbani A A novel method based on empirical mode decomposition for P300-based detection of deceptionIEEE Trans Inf For Secur2016 11 11 2584-2593.

Gao J, Lu L, Yang Y, Yu G, Na L, Rao N (2012) A novel concealed information test method based on independent component analysis and support vector machine. Clin EEG Neurosci 43:54–63

Ghosh-Dastidar S, Adeli H. A new supervised learning algorithm for multiple spiking neural networks with application in epilepsy and seizure detection. Neural Netw. 2009;22(10):1419–1431.

Srinivasa Sai Abhijit Challapalli. Optimizing Dallas-Fort Worth Bus Transportation System Using Any Logic. Journal of Sensors, IoT & Health Sciences, 2024; 2(4): 40-55.

Jung TP et al. Removing electroencephalographic artifacts by blind source separation. Psychophysiology. 2000;37(2):163–178.

Li Y, Guo Y, Zhang S, Deng S, Hai Y, Gu S. Differentiable spike: rethinking gradient-descent for training spiking neural networks. Adv Neural Inf Process Syst. 2021;34:23426. 29

Srinivasa Sai Abhijit Challapalli. Sentiment Analysis of the Twitter Dataset for the Prediction of Sentiments. Journal of Sensors, IoT & Health Sciences, 2024; 2(4): 1-15.

Luo Y, Shen H, Cao X, Wang T, Feng Q, Tan Z. Conversion of Siamese networks to spiking neural networks for energy-efficient object tracking. Neural Comput Appl. 2022;34(12):9967–9982.

Meixner JB, Rosenfeld JP. A mock terrorism application of the P300-based concealed information test. Psychophysiology. 2011;48(2):149–154.

K. Vinay Kumar, Sumanaswini Palakurthy, Sri Harsha Balijadaddanala, Sharmila Reddy Pappula, & Anil Kumar Lavudya. Early Detection and Diagnosis of Oral Cancer Using Deep Neural Network. Journal of Computer Allied Intelligence, 2024; 2(2): 22-34.

Ramadan RA, Vasilakos AV. Brain computer interface: control signals review. Neurocomputing. 2017;223:26–44.

Rosenfeld JP, Labkovsky E, Winograd M, Lui MA, Vandenboom C, Chedid E. The complex trial protocol (CTP): a new, countermeasure-resistant, accurate, P300-based method for detection of concealed information. Psychophysiology. 2008;45(6):906–919.