Tel Aviv University Center for Nanoscience and Nanotechnology, Tel Aviv University, Tel Aviv, Israel.
Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.
Sci Rep. 2018 Feb 1;8(1):2058. doi: 10.1038/s41598-018-20567-y.
Human facial expressions are a complex capacity, carrying important psychological and neurological information. Facial expressions typically involve the co-activation of several muscles; they vary between individuals, between voluntary versus spontaneous expressions, and depend strongly on personal interpretation. Accordingly, while high-resolution recording of muscle activation in a non-laboratory setting offers exciting opportunities, it remains a major challenge. This paper describes a wearable and non-invasive method for objective mapping of facial muscle activation and demonstrates its application in a natural setting. We focus on muscle activation associated with "enjoyment", "social" and "masked" smiles; three categories with distinct social meanings. We use an innovative, dry, soft electrode array designed specifically for facial surface electromyography recording, a customized independent component analysis algorithm, and a short training procedure to achieve the desired mapping. First, identification of the orbicularis oculi and the levator labii superioris was demonstrated from voluntary expressions. Second, the zygomaticus major was identified from voluntary and spontaneous Duchenne and non-Duchenne smiles. Finally, using a wireless device in an unmodified work environment revealed expressions of diverse emotions in face-to-face interaction. Our high-resolution and crosstalk-free mapping, along with excellent user-convenience, opens new opportunities in gaming, virtual-reality, bio-feedback and objective psychological and neurological assessment.
人类的面部表情是一种复杂的能力,承载着重要的心理和神经信息。面部表情通常涉及到多个肌肉的协同激活;它们在个体之间、自愿表达和自发表达之间有所不同,并且强烈依赖于个人的解释。因此,虽然在非实验室环境下高分辨率地记录肌肉激活提供了令人兴奋的机会,但这仍然是一个主要挑战。本文介绍了一种用于客观映射面部肌肉激活的可穿戴式、非侵入性方法,并展示了其在自然环境中的应用。我们专注于与“愉悦”、“社交”和“掩饰”微笑相关的肌肉激活;这三个类别具有不同的社交意义。我们使用了一种创新的、干式、柔软的电极阵列,专门用于面部表面肌电图记录,定制的独立成分分析算法,以及简短的训练程序来实现所需的映射。首先,从自愿表达中证明了眼轮匝肌和上唇提肌的识别。其次,从自愿和自发的杜兴和非杜兴微笑中识别出了颧大肌。最后,在未修改的工作环境中使用无线设备揭示了面对面互动中的各种情绪表达。我们的高分辨率、无串扰映射,以及出色的用户便利性,为游戏、虚拟现实、生物反馈以及客观的心理和神经评估开辟了新的机会。