Residuals of autoregressive model providing additional information for feature extraction of pattern recognition-based myoelectric control

Myoelectric control based on pattern recognition has been studied for several decades. Autoregressive (AR) features are one of the mostly used feature extraction methods among myoelectric control studies. Almost all previous studies only used the AR coefficients without the residuals of AR model for classification. However, the residuals of AR model contain important amplitude information of the electromyography (EMG) signals. In this study, we added the residuals to the AR features (AR+re) and compared its performance with the classical sixth-order AR coefficients. We tested six unilateral transradial amputees and eight able-bodied subjects for eleven hand and wrist motions. The classification accuracy (CA) of the intact side for amputee subjects and the right hand for able-bodied subjects showed that the CA of AR+re features was slightly but significantly higher than that of classical AR features (p = 0.009), which meant that residuals could provide additional information to classical AR features for classification. Interestingly, the CA of the affected side for amputee subjects showed that there was no significant difference between the CA of AR+re features and classical AR features (p > 0.05). We attributed this to the fact that the amputee subjects could not use their affected side to produce consistent EMG patterns as their intact side or the dominant hand of the able-bodied subjects. Since the residuals were already available when the AR coefficients were computed, the results of this study suggested adding the residuals to classical AR features to potentially improve the performance of pattern recognition-based myoelectric control.