Lie to my face: An electromyography approach to the study of deceptive behavior

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 their cheek muscles and those who activated their eyebrows. We found that the participants lied more often with time, with some switching their telltale muscle groups. Moreover, while the automated classifier, reported here, outperformed untrained human detectors, their performance was correlated, suggesting reliance on shared features.

Conclusions: Our findings demonstrate the feasibility of using wearable electrode arrays in detecting human lies in a social setting and set the stage for future research on individual differences in deception expression.

Keywords: cognition; electrophysiology; experimental psychology; psychology.

FIGURE 1

Setup, task procedure, and behavioral results. The participants completed a two‐person deception task, while their facial muscle activity was recorded using facial surface electromyography. (a) An eight‐electrode array was placed on each participant's face, with five electrodes recording from the zygomaticus major muscle (cheek region) and 3 from the corrugator supercilia muscle (eyebrows regions). (b) The participants took turns acting as Sender and Receiver. The Sender would hear a message via earphones (either “KAV” or “ETZ”; stimulus event), then would either repeat the word (Truth) or utter the other word (Lie; speech event). Then, the Receiver would indicate via key press whether they believed the Sender (Truth) or not (Lie). Depicted are two examples of trials: Lie‐Truth (top panel) and Truth‐Lie (bottom panel). (c) The participants lied in approximately half of the trials, and the frequency of lying increased between the two stages of the experiment (top panel). The Receivers’ detection of the Senders’ lying was at chance level, and time and monetary incentives did not change their performance (bottom panel)

FIGURE 2

Deception Detection Matrices (DDMs) of participants #05–13 and #20–41. Right panels: Each matrix element was derived from 1 s event‐related epoch from either the ZM or CR facial muscle regions, during stimulus, speech, or response. Red indicates a successful lie detection. Data were calculated for bins varying in duration (y‐axes) and sampled starting at varying time‐points relative to the event onset (x‐axes). Big panels: For each participant, the six DDMs were aggregated into a single matrix. Color coding was used to indicate muscle region and trigger (i.e., stimulus, speech, or response)

FIGURE 3

Multi‐subject result reveals two types of liars. (a) Results of a similarity analysis between the participants based on classification performance in each of the six DDMs (2 facial muscles × 3 trial events) based on surface electromyography (sEMG) data from the second stage of the experiment (with monetary incentives). A clustering algorithm identified two distinct groups of the participants based on similarity (blue and red squares). The IDs of the two exemplary participants from Figure 2 are highlighted. (b) The number of significant clusters in each of the six DDMs, averaged across the participants of each group. The differences suggest that the blue group's classification mostly relied on data from the eyebrow muscle (CS), and the red group has more classification success using data from the cheek muscle (ZM). (c) The ability of the classification algorithm to detect a participant's deception (measured as maximal classification accuracy) is negatively correlated with the ability of that participant to deceive their human counterpart (Receiver). Each circle represents a participant, colored based on their group belonging (as depicted in (a))