Discrete Versus Continuous Mapping of Facial Electromyography for Human–Machine Interface Control: Performance and Training Effects

Abstract

Individuals with high spinal cord injuries are unable to operate a keyboard and mouse with their hands. In this experiment, we compared two systems using surface electromyography (sEMG) recorded from facial muscles to control an onscreen keyboard to type five-letter words. Both systems used five sEMG sensors to capture muscle activity during five distinct facial gestures that were mapped to five cursor commands: move left, move right, move up, move down, and “click”. One system used a discrete movement and feedback algorithm in which the user produced one quick facial gesture, causing a corresponding discrete movement to an adjacent letter. The other system was continuously updated and allowed the user to control the cursor’s velocity by relative activation between different sEMG channels. Participants were trained on one system for four sessions on consecutive days, followed by one crossover session on the untrained system. Information transfer rates (ITRs) were high for both systems compared to other potential input modalities, both initially and with training (Session 1: 62.1 bits/min, Session 4: 105.1 bits/min). Users of the continuous system showed significantly higher ITRs than the discrete users. Future development will focus on improvements to both systems, which may offer differential advantages for users with various motor impairments.

Surface electromyographic control of a novel phonemic interface for speech synthesis

“…Muscle contractions during attempted facial gestures (e.g., left smile, eyebrow raise, wink) were translated, in real-time, into cursor movements (e.g., left, up, click; see Figure 1 for electrode placement). In that study, user performance in selecting targets on an alphabetic interface improved with training (Cler & Stepp, 2015). In a different study, the facial sEMG system was used by participants without motor impairments to produce speech by selecting phonemes on an onscreen phonemic AAC interface during one session (Cler, Nieto-Castanon, Guenther, & Stepp, 2014).…”

“…This technique, sEMG, provides a robust neural signal that can then be translated to cursor movements, leading to full 2D control of a computer (Choi, Rim, & Kim, 2011; Cler & Stepp, 2015; Larson, Terry, Canevari, & Stepp, 2013; Vernon & Joshi, 2011; Williams & Kirsch, 2008). sEMG signals can be translated to cursor movements with position-based algorithms, in which activation of one muscle corresponds to the cursor position in the horizontal direction, and activation of a different muscle corresponds to vertical cursor position or velocity-based algorithms, in which muscle activation corresponds to cursor velocity.…”

“…We have previously shown that participants without motor impairments could select targets using facial muscle contractions recorded via sEMG electrodes placed upon the facial skin (Cler & Stepp, 2015). Muscle contractions during attempted facial gestures (e.g., left smile, eyebrow raise, wink) were translated, in real-time, into cursor movements (e.g., left, up, click; see Figure 1 for electrode placement).…”

“…What has not yet been studied is the effect of training on performance with the sEMG cursor and a phonemic interface; furthermore, previous work with sEMG cursor control and phonemic interface has been restricted to studies with individuals without motor impairments. We hypothesize that improvements in performance will be seen in healthy individuals and in individuals who use AAC, both due to motor learning during the sEMG cursor control (as in Cler & Stepp, 2015) and due to faster visual search as participants become familiar with the layout of the phonemic targets.…”

Controle De Um Teclado Virtual Por Meio De Comandos Musculares Faciais

“…No Brasil, existem cerca de milhões de pessoas com algum tipo de deficiência, o que equivale a 23,9% da população, sendo 7% com alguma deficiência motora [1]. Uma parcela dessa população, devido a lesões na coluna espinhal a nível cervical ou desordens neurais, é incapaz de operar mouses e teclados convencionais, afetando negativamente muitos aspectos de sua qualidade de vida, incluindo a capacidade de se comunicar [2]. Essas pessoas necessitam de Tecnologias Assistivas (TA) especiais, onde os dispositivos são operados a partir de sensores que recebem informações fornecidas pelo usuário com deficiência para operar uma interface gráfica [3].…”