A Review on Artificial Intelligence methods and Signal Processing for EEG-Based lie and Truth Identification
DOI:
https://doi.org/10.58564/IJSER.3.2.2024.178Keywords:
Electroencephalogram, Deep learning, Machine learning, lie detectionAbstract
A false statement made with the goal of tricking someone is called a lie. Given how little there is to separate a falsehood from the truth, it can be difficult to tell the two apart. Lying requires more mental effort than telling the truth because the liar has to work hard to make the lie seem believable. When a person feels fear, anxiety, or extreme excitement, their oxygen consumption rate, blood pressure, galvanic skin resistance, and other physiological responses increase significantly. This is the basis for lie detection. In recent years, lie detection techniques have advanced beyond polygraphs to include methods such as electroencephalography, and analysis of eye blink patterns. In this work, we shall institute inspecting accurately Artificial Intelligence (AI) algorithms (Deep learning (DL) and machine learning (ML)) for built for EEG signal processing for lie detection. In this article, we reviewed literature from 2014 to 2023 to take previous and existing classification manner for EEG based-on the lie detection to focus and highlight newest in this field. In this review, we calculating more than 40 papers that utilized AI to EEG data for lie detection. The significance point of this paper is to helping the researchers and those interested in the field of deception detection to develop this field and make him more powerfully and effectively.
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