A Comparison of Surface and Intramuscular Myoelectric Signal Classification

By Levi J. Hargrove, Kevin B. Englehart, B. Hudgins. IEEE Transactions on Biomedical Engineering, 2007

548 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cellular and Molecular Neuroscience

View Paper: https://www.semanticscholar.org/paper/a6c1324cd53f2510d39501fad2b1d6ea4cdaa3a8

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In Biomedical Engineering

In Cellular and Molecular Neuroscience

Citations in the corpus

Myoelectric forearm prostheses: state of the art from a user-centered perspective. (2011, background): “Asghari Oskoei & Hu [36] 2008 SVM MAV Nondisabled (11) 200 6† 4 95”
A Strategy for Identifying Locomotion Modes Using Surface Electromyography (2009, methodology): “The simple LDA was reported as having comparable classification performance to more complex types [20]–[22] and as being computationally efficient for real-time myoelectric prosthesis control [8].”
Self-Recalibrating Surface EMG Pattern Recognition for Neuroprosthesis Control Based on Convolutional Neural Network (2017, background): “Surface electromyography (sEMG) has become a useful source of control signals for modern prosthetics due to its ease of use and non-invasiveness (Hargrove et al., 2007; Castellini and van der Smagt, 2009).”
Continuous Locomotion-Mode Identification for Prosthetic Legs Based on Neuromuscular–Mechanical Fusion (2011, methodology): “The LDA classifier is the most frequently used and commonly suggested classifier for EMG pattern recognition because it has been reported as having a comparable classification performance to more complex types [21], [23] and as being computationally efficient for real-”
Classification of Simultaneous Movements using Surface EMG Pattern Recognition (2012, unknown): “well as more complex nonlinear classifiers [29].”
Electromyogram pattern recognition for control of powered upper-limb prostheses: state of the art and challenges for clinical use. (2011, methodology): “[16] in their examination of the relative ef fects of features and c lassifiers on a co mmon data set (previously described in “EMG Site Selection” section).”
Determining the optimal window length for pattern recognition-based myoelectric control: balancing the competing effects of classification error and controller delay (2010, result): “The classification errors [ 23 ] and controllability [8] of this type of pattern recognition system has been compared to more complex control systems, where it was found to perform similarly.”
Decoding of Individuated Finger Movements Using Surface Electromyography (2009, background): “As far as the decoding of intramuscular myoelectric signals is concerned, Hargrove et al. [ 14 ] have recently shown that there was no significant difference when the decoding accuracy of the intramuscular EMG was compared to that of surface-based signals in wrist and grip movements.”