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Original Investigation |

Surface Electromyographic Mapping of the Orbicularis Oculi Muscle for Real-Time Blink Detection

Alice Frigerio, MD, PhD1,2; Paolo Cavallari, MD, PhD1; Marta Frigeni, MD1; Alessandra Pedrocchi, MSc, PhD3; Andrea Sarasola, MSc3; Simona Ferrante, MSc, PhD3
[+] Author Affiliations
1Human Physiology Section, Department of Pathophysiology and Transplantation, Università degli Studi di Milano, Milan, Italy
2Facial Nerve Center, Carolyn and Peter Lynch Center for Laser and Reconstructive Surgery, Division of Facial Plastic and Reconstructive Surgery, Department of Otology and Laryngology, Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston
3Neuroengineering and Medical Robotics Laboratory, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milan, Italy
JAMA Facial Plast Surg. 2014;16(5):335-342. doi:10.1001/jamafacial.2014.283.
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Importance  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.

Objectives  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.

Results  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  NA.

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Figure 1.
Electrode Placement

Channel 1 includes electrodes 1 and 2 (inferolateral quadrant). Channel 2 includes electrodes 2 and 3 (superolateral quadrant). Channel 3 includes electrodes 4 and 5 (superomedial quadrant). Channel 4 includes electrodes 5 and 6 (inferomedial quadrant).

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Figure 2.
Blink Electromyography (EMG) Signal and Thresholds

Shown is an extract of the EMG signal related to a blink, with definition of the 4 thresholds used by the real-time blink detection algorithm.

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Figure 3.
Blink Detection Algorithm

Shown is the flow of the real-time algorithm to detect blinking from surface electromyography (EMG).

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Figure 4.
Filtered Electromyography (EMG) Signals

Four blink peaks are presented for each of the 4 facial activities recorded in the experiments. Dots indicate the detected blinks.

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Figure 5.
Software Performance During Blinking and Other Facial Activities

Shown are the median (interquartile range) F1 scores obtained for each electromyographic channel and the 4 facial activities tested and the overall value.aIndicates a statistically significant difference (P < .05) between channels.

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