International Journal of Engineering and Technology

Lie or deception analysis is a significant challenge for investigators especially in crime cases. Identifying liar from normal human behaviour has higher relevance with external behaviour and cognitive functionality of human brain. Methods such as polygraph, cognitive polygraph, facial electromyography, eye tracking, voice stress analysis and functional magnetic resonance imaging have been already developed for deception analysis. Even these methods has its own merits, all these methods faces a common issue of accuracy in deception detection ratio due to different kind of liars and learned criminals. Thinking is an internal stimulus having relationship with deception. Identifying thinking responses from brain is one of the measures used to detect deception. Electroencephalography is another modality to understand cognitive responses such as thinking from human brain and this method can be extended to detect liar from other people. Statistical features such as Power, Variance and Root Mean Square (RMS) have been calculated for normal and thinking patterns of EEG signal. Primary objective of this paper is to focus various methods used for lie/deception analysis. This paper also explains influence of statistical features to discriminate thinking patterns from normal signal.