The wrist is one of the most flexible parts of the human body. By decomposing the surface electromyography (sEMG) signals, the deep neural drive of human body movements can be effectively reversed. Currently, research on wrist moments is basically focused on isometric contractions, and substantial research is still needed for the decoding of neural drives under dynamic conditions. This paper studied the decomposition of motor units (MUs) when the wrist moves under different resistances. Specifically, different resistance levels were set by using a magnetorheological damper. The collected complete electromyography signals were divided into short intervals where the changes in motor unit action potentials (MUAPs) are minimal. Then, a classic decomposition algorithm was used in each short interval to obtain the motor unit spike train (MUST), and the MUs were tracked through the overlapping parts of the short intervals, so as to obtain the complete firing sequence. This paper studied the decomposition of motor units when the wrist extended and flexed under three resistances of 20% maximum voluntary contraction (MVC), 40% MVC, and 60% MVC. The results showed that under the three types of resistances, the dynamic decomposition algorithm proposed in this paper could effectively decompose motor units (MUs) from the forearm electromyographic signals, and as the resistance increased, the decomposition effect decreased to some extent. During wrist extension, up to 10 ± 1 MUs could be decomposed, and the pulse-to-noise ratio (PNR) and silhouette distance (SIL) could reach 19.87 ± 1.42 dB and 0.91 ± 0.03 respectively. During wrist flexion, up to 22 ± 3 MUs could be decomposed, and the PNR and SIL values could reach 20.69 ± 2.14 dB and 0.92 ± 0.03 respectively. This study indicates that it is feasible to perform neural decoding from the EMG of wrist movements under different resistances, which has important implications for the application of high-density surface EMG (HD-sEMG) under dynamic contractions.