An ERP-based Guilty Knowledge Test for Sequential Lineups

Witnesses to crimes are sometimes reluctant to identify the culprit in a lineup (e.g., for fear of retribution). We introduce an ERP-based guilty knowledge test for sequential lineup identifications, using an oddball paradigm to evoke the P300 component when a witness sees a photo of a culprit compared to those evoked by an innocent familiar face. At the group level, clear differences were found between P300 amplitudes evoked by the culprit’s face and the innocent filler face. At the individual level, the participants’ waveforms were less diagnostic. This method of eyewitness assessment may prove useful if the procedure can be improved in ways that clarify P300 amplitudes for individual participants. Success in this endeavor would be best applied with witnesses who recognize the culprit easily but are compelled to claim falsely that they do not.

Deception Detection by Hybrid-Pair Wireless fNIRS System

Normal fNIRS setting up was limited by superficial physiological noises when applied into the deception detection. We designed a hybrid-pair wireless fNIRS system to improve the detection. The system takes advantages of short-pair channel to suppress the effect of physiological noises, and wireless module to improve the comfortableness of wearing it. We applied the system into a modified Guilty Knowledge Test. The experiment demonstrated that normal metrics might hint different energy consume during lying, while the regional oxygen saturation rSO2, specific in the system, is sensitive to indicate a lying.