Decoding of Individuated Finger Movements Using Surface Electromyography

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Myoelectric forearm prostheses: state of the art from a user-centered perspective. (2011, background): “Khushaba RN, Al-Jumaily A. Channel and feature selection in multifunction myoelectric control.”
Autonomy in Rehabilitation Robotics: An Intersection (2018, neutral): “Each decoded class maps to a distinct function, and the most advanced decoding techniques (which, importantly, also are usable by the human) discern 10-12 motion classes (22, 33).”
Feature reduction and selection for EMG signal classification (2012, methodology): “Moreover, most recent EMG signal classification studies have still employed set of feature vectors that carried a number of redundant features (e.g. Du, Lin, Shyu, & Tainsong, 2010; Farfán, Politti, & Felice, 2010; Khezri & Jahed, 2009; Khushaba, Al-Ani, & Al-Jumaily, 2010; Kim, Choi, Moon, & Mun, 2”
Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands (2016, methodology): “The sEMG electrodes are positioned in order to combine two methods that are common in the field, i.e., a dense sampling approach (Fukuda et al., 2003; Tenore et al., 2009; Li et al., 2010) and a precise anatomical positioning strategy (De Luca, 1997; Castellini et al., 2009).”