Purpose: The primary aim of this study is to investigate the potential benefit of the Teager–Kaiser Energy Operator (TKEO) as data pre-processor, in an autonomous burst detection method to classify electromyographic signals of the (fore)arm and hand. For this purpose, optimal settings of the burst detector, leading to minimal detection errors, need to be known. Additionally, the burst detector is applied to real muscle activity recorded in healthy adults performing reach-to-grasp movements. Methods: The burst detector was based on the Approximated Generalized Likelihood Ratio (AGLR). Simulations with synthesized electromyographic (EMG) traces with known onset and offset times, yielded optimal settings for AGLR parameters “window width” and “threshold value” that minimized detection errors. Next, comparative simulations were done with and without TKEO data pre-processing. Correct working of the burst detector was verified by applying it to real surface EMG signals obtained from arm and hand muscles involved in a sub-maximal reach-to-grasp task, performed by healthy adults. Results: Minimal detection errors were found with a window width of 100 ms and a detection threshold of 15. Inclusion of the TKEO contributed significantly to a reduction of detection errors. Application of the autonomous burst detector to real data was feasible. Conclusions: The burst detector was able to classify muscle activation and create Muscle Onset Offset Profiles (MOOPs) autonomously from real EMG data, which allows objective comparison of MOOPs obtained from movement tasks performed in different conditions or from different populations. The TKEO contributed to improved performance and robustness of the burst detector.