Multi-channel electromyography-based mapping of spontaneous smiles.

Objective: Human facial muscle activation underlies highly sophisticated signaling mechanisms that are critically important for healthy physiological function. Accordingly, the necessity to analyze facial muscle activation at high-resolution and in a non-invasive manner is important for the diagnosis and treatment of many medical conditions. However, current clinical examination methods are neither precise nor quantitative.

Approach: Wearable, multi-channel surface electromyography can provide a solution to this yet unmet challenge. Here, we present the design and testing of a customized surface electromyography electrode array for facial muscle mapping.

Main Results: Muscle activation maps were derived from repeated voluntary facial muscle activations. A customized independent component analysis algorithm and a clustering algorithm were developed to identify consistent building block activation patterns within and between participants. Finally, focusing on spontaneous smile analysis and relying on the building block mapping, we classified muscle activation sources, revealing a consistent intra-subject activation and an inter-subject variability.

Significance: The herein described approach can be readily used for automated and objective mapping of facial expressions in general and in the assessment of normal and abnormal smiling in particular.