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ABSTRACT: Background
Deception is present in all walks of life, from social interactions to matters of homeland security. Nevertheless, reliable indicators of deceptive behavior in real-life scenarios remain elusive.Methods
By integrating electrophysiological and communicative approaches, we demonstrate a new and objective detection approach to identify participant-specific indicators of deceptive behavior in an interactive scenario of a two-person deception task. We recorded participants' facial muscle activity using novel dry screen-printed electrode arrays and applied machine-learning algorithms to identify lies based on brief facial responses.Results
With an average accuracy of 73%, we identified two groups of participants: Those who revealed their lies by activating thei
SUBMITTER: Shuster A
PROVIDER: S-EPMC8671780 | biostudies-literature | 2021 Dec
REPOSITORIES: biostudies-literature