Facial paralysis is a life-altering condition that significantly impairs function, appearance, and communication. Facial rehabilitation via closed-loop pacing represents a potential but as yet theoretical approach to reanimation. A first critical step toward closed-loop facial pacing in cases of unilateral paralysis is the detection of healthy movements to use as a trigger to prosthetically elicit automatic artificial movements on the contralateral side of the face.
To test and to maximize the performance of an electromyography (EMG)-based blink detection system for applications in closed-loop facial pacing.
Design, Setting, and Participants
Blinking was detected across the periocular region by means of multichannel surface EMG at an academic neuroengineering and medical robotics laboratory among 15 healthy volunteers.
Main Outcomes and Measures
Real-time blink detection was accomplished by mapping the surface of the orbicularis oculi muscle on one side of the face with a multichannel surface EMG. The biosignal from each channel was independently processed; custom software registered a blink when an amplitude-based or slope-based suprathreshold activity was detected. The experiments were performed when participants were relaxed and during the production of particular orofacial movements. An F1 score metric was used to analyze software performance in detecting blinks.
The maximal software performance was achieved when a blink was recorded from the superomedial orbit quadrant. At this recording location, the median F1 scores were 0.89 during spontaneous blinking, 0.82 when chewing gum, 0.80 when raising the eyebrows, and 0.70 when smiling. The overall performance of blink detection was significantly better at the superomedial quadrant (F1 score, 0.75) than at the traditionally used inferolateral quadrant (F1 score, 0.40) (P < .05).
Conclusions and Relevance
Electromyographic recording represents an accurate tool to detect spontaneous blinks as part of closed-loop facial pacing systems. The early detection of blink activity may allow real-time pacing via rapid triggering of contralateral muscles. Moreover, an EMG detection system can be integrated in external devices and in implanted neuroprostheses. A potential downside to this approach involves cross talk from adjacent muscles, which can be notably reduced by recording from the superomedial quadrant of the orbicularis oculi muscle and by applying proper signal processing.
Level of Evidence