Estimates of Classification Complexity for Myoelectric Pattern Recognition

Myoelectric pattern recognition (MPR) can be used for intuitive control of virtual and robotic effectors in clinical applications such as prosthetic limbs and the treatment of phantom limb pain. The conventional approach is to feed classifiers with descriptive electromyographic (EMG) features that represent the aimed movements. The complexity and consequently classification accuracy of MPR is highly affected by the separability of such features. In this study, classification complexity estimating algorithms were investigated as a potential tool to estimate MPR performance. An early prediction of MPR accuracy could inform the user of faulty data acquisition, as well as suggest the repetition or elimination of detrimental movements in the repository of classes. Two such algorithms, Nearest Neighbor Separability (NNS) and Separability Index (SI), were found to be highly correlated with classification accuracy in three commonly used classifiers for MPR (Linear Discriminant Analysis, Multi-Layer Perceptron, and Support Vector Machine). These Classification Complexity Estimating Algorithms (CCEAs) were implemented in the open source software BioPatRec and are available freely online. This work deepens the understanding of the complexity of MPR for the prediction of motor volition.