Differences in brain activations between micro- and macro-expressions based on electroencephalography

Abstract

Micro-expressions can reflect an individual's subjective emotions and true mental state and are widely used in the fields of mental health, justice, law enforcement, intelligence, and security. However, the current approach based on image and expert assessment-based micro-expression recognition technology has limitations such as limited application scenarios and time consumption. Therefore, to overcome these limitations, this study is the first to explore the brain mechanisms of micro-expressions and their differences from macro-expressions from a neuroscientific perspective. This can be a foundation for micro-expression recognition based on EEG signals. We designed a real-time supervision and emotional expression suppression (SEES) experimental paradigm to synchronously collect facial expressions and electroencephalograms. Electroencephalogram signals were analyzed at the scalp and source levels to determine the temporal and spatial neural patterns of micro- and macro-expressions. We found that micro-expressions were more strongly activated in the premotor cortex, supplementary motor cortex, and middle frontal gyrus in frontal regions under positive emotions than macro-expressions. Under negative emotions, micro-expressions were more weakly activated in the somatosensory cortex and corneal gyrus regions than macro-expressions. The activation of the right temporoparietal junction (rTPJ) was stronger in micro-expressions under positive than negative emotions. The reason for this difference is that the pathways of facial control are different; the production of micro-expressions under positive emotion is dependent on the control of the face, while micro-expressions under negative emotions are more dependent on the intensity of the emotion.

Keywords: electroencephalography (EEG); emotion; expression inhibition; macro-expressions; micro-expressions.

FIGURE 1

Schematic representation of the presentation of a single block video material.

FIGURE 2

Diagram of facial expression identification and determination of the time points of micro-expressions.

FIGURE 3

Topographical maps for EEG power in micro-expression and macro-expression from theta, alpha, beta and gamma bands in (A) Positive emotion and (Micro-expression minus Macro-expression) (B) Negative emotion (Macro-expression minus Micro-expression). The white dots on the difference graph are the electrode points with significant difference between the power of micro-expression and macro-expression.

FIGURE 4

LORETA probabilistic map showing cortical activation and a significant difference between micro-expression minus macro-expression in (A) Positive emotion and (B) Negative emotion (Micro-expression minus Macro-expression) (B) Negative emotion (Macro-expression minus Micro-expression). Red colors represent a greater activation, blue colors represent a less activation.

FIGURE 5

Topographical maps for EEG power and LORETA probabilistic map showing cortical activation and a significant difference between micro-expression under positive emotion and negative emotion (positive emotion minus negative emotion). Red colors represent a greater activation, blue colors represent a less activation. The white dots are the electrode points with significant difference.