Prediction of Optimal Facial Electromyographic Sensor Configurations for Human-Machine Interface Control

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 26; no. 8; pp. 1566 - 1576
• Main Authors: Vojtech, Jennifer M., Cler, Gabriel J., Stepp, Cara E.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.08.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Calibration; Configuration management; Configurations; Control systems; Electrodes; Electromyography; Error detection; Factor analysis; Feature extraction; Human performance; human-machine interfaces; Muscles; myoelectric control; Performance prediction; Recording; Sensors; Skin; Task analysis; Task complexity
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2018.2849202

Surface electromyography (sEMG) is a promising computer access method for individuals with motor impairments. However, optimal sensor placement is a tedious task requiring trial-and-error by an expert, particularly when recording from facial musculature likely to be spared in individuals with neurological impairments. We sought to reduce the sEMG sensor configuration complexity by using quantitative signal features extracted from a short calibration task to predict human-machine interface (HMI) performance. A cursor control system allowed individuals to activate specific sEMG-targeted muscles to control an onscreen cursor and navigate a target selection task. The task was repeated for a range of sensor configurations to elicit a range of signal qualities. Signal features were extracted from the calibration of each configuration and examined via a principle component factor analysis in order to predict the HMI performance during subsequent tasks. Feature components most influenced by the energy and the complexity of the EMG signal and muscle activity between the sensors were significantly predictive of the HMI performance. However, configuration order had a greater effect on performance than the configurations, suggesting that non-experts can place sEMG sensors in the vicinity of usable muscle sites for computer access and healthy individuals will learn to efficiently control the HMI system.