Exploring intrinsic triggers for functional facial electrostimulation based on intramuscular electromyography...

Based on univariate intramuscular electromyography (EMG) recordings of facial muscles of patients suffering from chronic idiopathic facial palsy we propose a data-driven feature selection process for the discrimination of different mimic maneuvers. Following fundamental ideas of automatic EMG decompositions based on templates defined by motor unit action potentials, the proposed approach relies on a multiple template matching. Yet, the novel methodology utilizes templates derived from the intramuscular EMG signal itself without any supervisor interaction or a priori information by identifying abundant short signal sections (motifs). Focusing on motifs as individual, characteristical graphoelements of an EMG recording implies a high level of flexibility. In connection with facial palsy such a flexibility is necessary, since unique individual, also pathological, EMG patterns can be expected due to the high spatial variability of intramuscular recordings combined with random patterns of aberrant reinnervation. The proposed methodology is applied to EMG data of frontalis, zygomaticus, and orbicularis oculi muscle without patient- or muscle-specific adaptations.