fNIRS-based online deception decoding

Deception involves complex neural processes in the brain. Different techniques have been used to study and understand brain mechanisms during deception. Moreover, efforts have been made to develop schemes that can detect and differentiate deception and truth-telling. In this paper, a functional near-infrared spectroscopy (fNIRS)-based online brain deception decoding framework is developed. Deploying dual-wavelength fNIRS, we interrogate 16 locations in the forehead when eight able-bodied adults perform deception and truth-telling scenarios separately. By combining preprocessed oxy-hemoglobin and deoxy-hemoglobin signals, we develop subject-specific classifiers using the support vector machine. Deception and truth-telling states are classified correctly in seven out of eight subjects. A control experiment is also conducted to verify the deception-related hemodynamic response. The average classification accuracy is over 83.44% from these seven subjects. The obtained result suggests that the applicability of fNIRS as a brain imaging technique for online deception detection is very promising.