High-density surface electromyography (HD-sEMG) decomposition enables non-invasive investigation of motor unit behavior but is limited in clinical application by the need for high-density electrode arrays. This study investigated whether decomposition performance can be maintained at reduced channel density by combining Progressive FastICA Peel-off (PFP) and Convolution Kernel Compensation (CKC). Using 64-channel HD-sEMG data, parallel and sequential extraction strategies, with and without CKC refinement, were evaluated on a public database. The results indicate that parallel extraction based on FastICA provides robust motor unit decomposition under reduced channel conditions, whereas sequential extraction shows markedly lower performance. CKC refinement exhibited method-dependent effects and did not improve overall performance at low channel density. The findings demonstrate that reliable HD-sEMG decomposition is achievable with substantially fewer channels when an appropriate algorithm is selected, highlighting the dominant role of decomposition strategy over subject-specific factors.