Abstract
This paper presents a method to identify lying automatically using EEG signals. The wavelet entropy of event-related potentials (ERP) carries information about the degree of order associated with a multi-frequency brain electrophysiological activity. We used wavelet entropy to analyze ERP during a lying task. Ten subjects were divided into guilty and innocent groups randomly. They were instructed to make a truthful or deceptive responses on the stimuli. EEG recordings on Pz channel were collected and the features of wavelet entropy were extracted. Statistical result reveals that there is significantly lower wavelet entropy value for the guilty group than that for the control group. We concluded that guilty subjects showed much high order degree of the brain state than normal persons after about 300 ms after stimulus onset. Hence, wavelet entropy is an effective and reliable approach to detect deception, and can help us to understand cognition processing deeply for lying behaviors.
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Acknowledgment
The work was supported by the National Nature Science Foundation of China (No. 81271659 and 61773408), the China Postdoctoral Science Foundation (No. 2014M552346).
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Xiong, Y., Gao, J., Chen, R. (2018). Wavelet Entropy Analysis for Detecting Lying Using Event-Related Potentials. In: Yuan, H., Geng, J., Liu, C., Bian, F., Surapunt, T. (eds) Geo-Spatial Knowledge and Intelligence. GSKI 2017. Communications in Computer and Information Science, vol 848. Springer, Singapore. https://doi.org/10.1007/978-981-13-0893-2_46
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DOI: https://doi.org/10.1007/978-981-13-0893-2_46
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