Thinking Outside the Button Box: EMG as a Computer Input Device for Psychological Research

Abstract

Experimental psychology research commonly has participants respond to stimuli by pressing buttons or keys. Standard computer input devices constrain the range of motoric responses participants can make, even as the field advances theory about the importance of the motor system in cognitive and social information processing. Here we describe an inexpensive way to use an electromyographic (EMG) signal as a computer input device, enabling participants to control a computer by contracting muscles that are not usually used for that purpose, but which may be theoretically relevant. We tested this approach in a study of facial mimicry, a well-documented phenomenon in which viewing emotional faces elicits automatic activation of corresponding muscles in the face of the viewer. Participants viewed happy and angry faces and were instructed to indicate the emotion on each face as quickly as possible by either furrowing their brow or contracting their cheek. The mapping of motor response to judgment was counterbalanced, so that one block of trials required a congruent mapping (contract brow to respond "angry," cheek to respond "happy") and the other block required an incongruent mapping (brow for "happy," cheek for "angry"). EMG sensors placed over the left corrugator supercilii muscle and left zygomaticus major muscle fed readings of muscle activation to a microcontroller, which sent a response to a computer when activation reached a predetermined threshold. Response times were faster when the motor-response mapping was congruent than when it was incongruent, extending prior studies on facial mimicry. We discuss further applications of the method for research that seeks to expand the range of human-computer interaction beyond the button box.

SDAV 1.0: A Low-Cost sEMG Data Acquisition & Processing System For Rehabilitatio

“…We utilize an MWM sensor with an Arduino Uno R3 Microcontroller to develop our initial single-channel (extended to three-channel) sEMG data acquisition to meet the objective of ease of availability, accessibility, low cost, and experimentation on the human face and neck. Fig 3 depicts the MWM sensor's details [22,23]. We selected the MWM sensor (AT-04-001), a wearable sensor with a single supply (+2.9V to +5.7V), two output modes, and an adjustable gain; such features are helpful for a prosthetic, orthosis, and other control systems.…”

“…Various research groups have investigated the complicated structure of sEMG signals using commercial or low-cost setups. Witman et al [7,8] examined it for recognizing finger movement and interpreting the alphabet of sign language; Kumar et al [9], Arjunan et al [10,11], Naik et al [12], Meltzner et al [13], Larraz et al [14], Agnihotri et al [15], Vyas et al [16], Kachhwaha et al [17], and Chandrashekhar [18] explored it for silent speech content recognition; Russo et al [19] studied it for a prosthetic robotic hand; Sidik et al [20] and Kareem et al [21] probed it to acquire lower arm motion, and Crawford et al [22] used it to capture facial expressions. Due to its non-invasive, safe, and effective method for measuring muscle activity, sEMG is a valuable tool for evaluating muscle function, diagnosing muscle disorders, monitoring muscle activity during physical activity, and designing ergonomic equipment.…”

“…The MWM sensor is a small, low-cost, and flexible sensor that can be placed directly on the skin to detect the electrical activity of a muscle. Several research findings [7,8,[18][19][20][21][22] have shown the MWM sensor's dependability and suitability for usage in sEMG data acquisition. The MWM sensor has the added benefit of being far less expensive than the commercial sEMG recording setup.…”

Emotion Recognition with Forearm-based Electromyography

“…Their study does not portray the possibility of detecting emotion just based on EMG data. Regarding applications of this research domain, in (Crawford, Vavra, & Corbin, 2017) they have built a device also using myosensors to interact with a computer using facial muscles and facial mimicry. But these approach has limiting nature because it only uses facial muscles where the muscle movements are strict.…”