Classification complexity in myoelectric pattern recognition.
Background: Limb prosthetics, exoskeletons, and neurorehabilitation devices can be intuitively controlled using myoelectric pattern recognition (MPR) to decode the subject's intended movement. In conventional MPR, descriptive electromyography (EMG) features representing the intended movement are fed...
| Published in: | Journal of NeuroEngineering & Rehabilitation (JNER) Vol. 14; no. 1; pp. 1 - 19 |
|---|---|
| Main Authors: | , , |
| Format: | research Journal Article |
| Published: |
BioMed Central
7/10/2017
|
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=124073934&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 124073934 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 17430003 1CUC jtl: Journal of NeuroEngineering & Rehabilitation (JNER) issn: 17430003 maglogo: N pubinfo: dt: 7/10/2017 vid: 14 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 124073934 124073934 NLM28693533 124073934 10.1186/s12984-017-0283-5 NLM28693533 124073934 ppf: 1 ppct: 18 formats: tig: atl: Classification complexity in myoelectric pattern recognition. aug: au: Nilsson, Niclas Håkansson, Bo Ortiz-Catalan, Max affil: Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden. sug: subj: Information Science Methods Brain-Computer Interfaces Electromyography Classification Research Subjects Neural Networks (Computer) Electromyography Methods Signal Processing, Computer Assisted Grip Strength Physiology Prosthesis Design Volition Algorithms Movement Reproducibility of Results Discriminant Analysis Human ab: Background: Limb prosthetics, exoskeletons, and neurorehabilitation devices can be intuitively controlled using myoelectric pattern recognition (MPR) to decode the subject's intended movement. In conventional MPR, descriptive electromyography (EMG) features representing the intended movement are fed into a classification algorithm. The separability of the different movements in the feature space significantly affects the classification complexity. Classification complexity estimating algorithms (CCEAs) were studied in this work in order to improve feature selection, predict MPR performance, and inform on faulty data acquisition.Methods: CCEAs such as nearest neighbor separability (NNS), purity, repeatability index (RI), and separability index (SI) were evaluated based on their correlation with classification accuracy, as well as on their suitability to produce highly performing EMG feature sets. SI was evaluated using Mahalanobis distance, Bhattacharyya distance, Hellinger distance, Kullback-Leibler divergence, and a modified version of Mahalanobis distance. Three commonly used classifiers in MPR were used to compute classification accuracy (linear discriminant analysis (LDA), multi-layer perceptron (MLP), and support vector machine (SVM)). The algorithms and analytic graphical user interfaces produced in this work are freely available in BioPatRec.Results: NNS and SI were found to be highly correlated with classification accuracy (correlations up to 0.98 for both algorithms) and capable of yielding highly descriptive feature sets. Additionally, the experiments revealed how the level of correlation between the inputs of the classifiers influences classification accuracy, and emphasizes the classifiers' sensitivity to such redundancy.Conclusions: This study deepens the understanding of the classification complexity in prediction of motor volition based on myoelectric information. It also provides researchers with tools to analyze myoelectric recordings in order to improve classification performance. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|