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

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.