*2.5. Feature Extraction Procedure*

In this study, a total of sixteen feature extraction methods (including two feature sets proposed previously by our research group) that have been applied for characterizing multi-classes of targeted limb movement intent were selected from four functional EMG feature groups namely: time-domain, frequency-domain, time-series domain, and the statistical features. Furthermore, four feature sets are based on EMG signal amplitude, five are based on nonlinear complexity and frequency information, two are based on time-series modelling, and the remaining five are based on combination of feature sets (Table 2).





It should be noted that features from the above described categories were considered to adequately account for all possible types of meaningful information associated with EMG signal classification [5]. Meanwhile, the accuracy, computational complexity, and robustness of the feature extraction methods were systematically investigated for each combination of window length and increment presented in Table 1 using a number of evaluation metrics described in the Section 2.6.
