Classification of facial movements in chronic facial palsy based on intramuscular EMG signals recorded from the paretic side.

Patients suffering from chronic facial palsy are frequently impaired by severe life-long dysfunctions. Thus, the loss of the ability to close eyes rapidly and completely bears the risk of corneal damages. Moreover, the loss of smile and an altered facial expression imply psychological stress and impede a healthy social life. Since surgical and conservative treatments frequently do not solve many problems sufficiently, closed-loop neural prosthesis are considered as feasible approach. For it, amongst others a reliable detection of the currently executed facial movement is necessary. In our proof of concept study, we propose a data-driven feature extraction for classifying eye closures and smile based on intramuscular EMGs from orbicularis oculi and zygomaticus muscles of the patient's palsy side. The data-adaptive nature of the approach enables a flexible applicability to different muscles and subjects without patient-or muscle-specific adaptations.