This paper studies the time-dependent power spectral density (PSD) estimation of nonstationary surface electromyography (SEMG) signals and its application to fatigue analysis during isometric muscle contraction. The conventional time-dependent PSD estimation methods exhibit large variabilities in estimating the instantaneous SEMG parameters so that they often fail to identify the changing patterns of short-period SEMG signals and gauge the extent of fatigue in specific muscle groups. To address this problem, a time-varying autoregressive (TVAR) model is proposed in this paper to describe the SEMG signal, and then the recursive least-squares (RLS) and basis function expansion (BFE) methods are used to estimate the model coefficients and the time-dependent PSD. The instantaneous parameters extracted from the PSD estimation are evaluated and compared in terms of reliability, accuracy, and complexity. Experimental results on synthesized and real SEMG data show that the proposed TVAR-model-based PSD estimators can achieve more stable and precise instantaneous parameter estimation than conventional methods.