Concealed Information Detection Using EEG for Lie Recognition by ERP P300 in Response to Visual Stimuli: a Review

Žabčíková, Martina; Koudelková, Zuzana; Jašek, Roman

doi:10.37394/23209.2022.19.17

Cited by 1 publication

(1 citation statement)

References 30 publications

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“…Hence, lie detection must be done carefully, considering the various factors affecting the detection results. The existing lie detector tools are based on previous research is a polygraph that measures the response of the nervous system [1], EEG which is a signal the brain uses to recognize information hidden in the brain to detect lies, but the EEG method has a broader application [2], [3]. Several methods and algorithms are used in research on lie detection in speech, including SVM models, Bayesian models (BN), conditional random field models (CRFM), DBN, CNN, LSTM, and RNN [4],…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Feature Mel Frequency Cepstral Coefficient and Short Time Fourier Transform Input for Lie Detection using Convolutional Neural Network

Kusumawati,

Ilham,

Achmad

et al. 2024

JOIV : Int. J. Inform. Visualization

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This study aims to determine which model is more effective in detecting lies between models with Mel Frequency Cepstral Coefficient (MFCC) and Short Time Fourier Transform (STFT) processes using Convolutional Neural Network (CNN). MFCC and STFT processes are based on digital voice data from video recordings that have been given lie or truth information regarding certain situations. Data is then pre-processed and trained on CNN. The results of model performance evaluation with hyper-tuning parameters and random search implementation show that using MFCC as Voice data processing provides better performance with higher accuracy than using the STFT process. The best parameters from MFCC are obtained with filter convolutional=64, kerneconvolutional1=5, filterconvolutional2=112, kernel convolutional2=3, filter convolutional3=32, kernelconvolutional3 =5, dense1=96, optimizer=RMSProp, learning rate=0.001 which achieves an accuracy of 97.13%, with an AUC value of 0.97. Using the STFT, the best parameters are obtained with filter convolutional1=96, kernel convolutional1=5, convolutional2 filters=48, convolutional2 kernels=5, convolutional3 filters=96, convolutional3 kernels=5, dense1=128, Optimizer=Adaddelta, learning rate=0.001, which achieves an accuracy of 95.39% with an AUC value of 0.95. Prosodics are used to compare the performance of MFCC and STFT. The result is that prosodic has a low accuracy of 68%. The analysis shows that using MFCC as the process of sound extraction with the CNN model produces the best performance for cases of lie detection using audio. It can be optimized for further research by combining CNN architectural models such as ResNet, AlexNet, and other architectures to obtain new models and improve lie detection accuracy.

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