Le décodage des expressions faciales émotionnelles à travers différentes bandes de fréquences spatiales et ses interactions avec l’anxiété
The decoding of emotional facial expressions (EFE) is a key function of the human visual system since it lays at the basis of non-verbal communication that allows social interactions. Numerous studies suggests that the processing of faces diagnostic features may take place differently for low and high spatial frequencies (SF), respectively in the magno- and parvocellular pathways. Moreover, conditions such as social anxiety are supposed to influence this processing and the associated event-related potentials (ERP). This study explores the feasibility of predicting social anxiety levels using electrophysiological correlates of EFE processing across various SF bands. To this end, ERP from 26 participants (mean age = 23.7 ± 4.7) years old were recorded during visual presentation of neutral, angry and happy facial expressions, filtered to retain only low, medium or high SF. Social anxiety was previously assessed using the LSAS questionnary. Peak latencies and amplitudes of the P100, N170, N2b/P3a complex and P3b components were statistically analyzed and used to feed supervised machine learning algorithms. P100 amplitude was linked to SF content. N170 was effected by EFE. N2b/P3a complex was larger for EFE and earlier for high SF. P3b was lower for neutral faces, which were also more often omitted. The linear discriminant analysis showed a decoding accuracy across significant features with a mean of 56.11%. The nature of these features and their sensitivity to social anxiety will be discussed.
Emotional facial expressions Spatial frequencies Electroencephalography Event-related potentials Machine learning Linear discriminant analysis Social anxiety