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- W2807794920 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." @default.
- W2807794920 created "2018-06-21" @default.
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- W2807794920 date "2018-01-01" @default.
- W2807794920 modified "2023-09-24" @default.
- W2807794920 title "Wavelet Entropy Analysis for Detecting Lying Using Event-Related Potentials" @default.
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- W2807794920 doi "https://doi.org/10.1007/978-981-13-0893-2_46" @default.
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