title: On the robustness of EMG features for pattern recognition based myoelectric control: a multi-dataset comparison.
abstract: The selection of optimal features has long been a subject of debate for pattern recognition based myoelectric control. Studies have compared many features, but have typically used small or constrained data sets. Herein, the performance of various features is evaluated using data from six previously reported data sets. The number of channels, the contraction dynamics (dynamic vs static), and classifier type all yielded significant interactions (p<;0.05) with the feature set. When using 8 channels, the addition of the tested features produced no improvement over a standard time domain (TD) feature set alone (p>0.05). When using fewer channels, however, autoregressive, Cepstral coefficients, Willison amplitude and sample entropy features all provided significant improvement during dynamic contractions (p<;0.05). The simple Willison amplitude is highlighted, showing that it can provide significant improvement when used as a replacement for any one of the standard TD features.
authors.forename: E
authors.initials: E
authors.lastname: Scheme
authors.forename: K
authors.initials: K
authors.lastname: Englehart
mesh_terms: D000465:Algorithms; D004576:Electromyography; D005542:Forearm / Q000502:physiology*; D006801:Humans; D016014:Linear Models; D010363:Pattern Recognition, Automated; D060388:Support Vector Machine