Optimal processing of surface facial EMG to identify emotional expressions: A data-driven approach.

ABSTRACT: Surface facial electromyography (EMG) is commonly used to detect emotions from subtle facial expressions. Although there are established procedures for collecting EMG data and some aspects of their processing, there is little agreement among researchers about the optimal way to process the EMG signal, so that the study-unrelated variability (noise) is removed, and the emotion-related variability is best detected. The aim of the current paper was to establish an optimal processing pipeline for EMG data for identifying emotional expressions in facial muscles. We identified the most common processing steps from existing literature and created 72 processing pipelines that represented all the different processing choices. We applied these pipelines to a previously published dataset from a facia

SUBMITTER: Rutkowska JM 

PROVIDER: S-EPMC11362446 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature