A robust surface EMG decomposition tool, referred to as tremor-optimized Convolution Kernel Compensation (CKC) technique, is described. This technique modifies and extends the previously published CKC method in order to circumvent the typical assumption on regularity and asynchrony of motor unit firings in normal condition and adapt to the discharge patterns in pathological tremor. The results on synthetic and experimental surface EMG signals demonstrate high performance of decomposition. In the case of simulated surface EMG with 20 dB SNR, excitation level of 20% maximum voluntary contraction (MVC) and simulated tremor frequency of 8 Hz, the newly proposed method identified 8 ± 2 motor units with sensitivity of motor unit discharge identification ≥ 95 % and false alarm and miss rates ≤ 5%. The performance worsened with increasing noise power, with 5 ± 2 motor units identified at 10 dB SNR and 3 ± 1 at 0 dB SNR. In 24 recordings of high-density surface EMG signals from four tremor-affected patients, the modified CKC technique identified 134 motor units (6 ± 4 motor units per contraction).