Exploring optimal myoelectric feature indices for forearm control strategy using robust principal component analysis.

Forearm movements realize various functions needed in daily life. For reproduction of the motion sequences, active myoelectric devices have been developed. Usually, feature indices are extracted from observed signals in control strategy; however, the optimal combination of indices is still unclear. This paper introduces sparsity-inducing penalty term in principal component analysis (PCA) to explore optimal myoelectric feature indices. An electromyographic database including seven forearm movements from 30 subjects was used for performance comparison. Linear classifier with sparse features showed best performance (7.86±3.82% error rate) that was significantly better than linear classifier with all features because of recovering low rank matrix in original data. Furthermore, the sparse features had a large contribution of underlying data structure with less number of principal components than PCA. Root-mean-square, time-domain features, autoregressive coefficients, and Histogram purported to be important in projected feature space; therefore, the feature indices are important to myoelectric strategies.