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Detecting the guilty individual in a theft among normal people is an essential part of the criminal justice system. The main objective of this study is to detect the guilty individual of theft by analyzing their neural correlates using the electroencephalographic technique. Guilty and normal group are defined as in the Guilty Knowledge Test. The wavelet packet decomposition is utilized to obtain the brain waves such as alpha, beta, and theta waves, and their respective relative wavelet packet energies are computed. Mann-Whitney U statistical test illustrates that the thief is attentive and conscious about the guilt through the theta and beta wave energy from the right parietal lobe. The memory about the target stimulus and theft that is processed in the left temporal lobe of a guilty individual is shown by theta wave energy. The thief inhibiting his/her feedback while seeing the target is realized by beta wave energy from the left temporal lobe and alpha wave energy from the right parietal lobe. Moreover, event-related potential component and functional brain connectivity patterns outlined the neural correlates of guilty group and significance of the temporal and parietal lobes . This study evidences the effectiveness of neural correlates of guilt knowledge analysis.

Neural correlates of guilt knowledge analysis using electroencephalographic technique