This study examines the application of time-frequency analysis (TFA) techniques in the analysis of complex bioelectrical signals, particularly electromyography (EMG) signals. Facing challenges in processing non-stationary EMG signals due to the limitations in time-frequency resolution and energy concentration of Wavelet Transform (WT), Synchrosqueezing Transform (SST), and Synchroextracting Transform (SET), this paper introduces an advanced technique known as High-Order Synchroextracting Wavelet Transform (HSEWT). By leveraging high-order instantaneous frequency estimation and time-frequency reallocation techniques, HSEWT significantly enhances the precision in characterizing time-varying features of EMG signals, as well as the energy concentration and resolution of time-frequency representation. Numerical simulations were conducted to assess the performance of the proposed method in analyzing typical amplitude-modulated and frequency-modulated (AM-FM) multi-component signals. Experimental results confirm the effectiveness of the HSEWT technique in improving the accuracy and reliability of EMG signal analysis, offering new avenues for the diagnosis and treatment of neuromuscular diseases.