The recognition process encompasses three fundamental stages: (1) data pre-processing, which entails noise removal from raw signals and segmentation of extended signals; (2) feature extraction, involving the derivation of temporal, spectral, time-frequency, spatial, or high-dimensional semantic features utilizing deep learning networks; (3) pattern recognition, encompassing the identification and classification of signals through deep learning networks or machine learning methodologies. It is imperative to emphasize the pivotal role played by feature extraction in this overall process. Time-domain feature analysis [15], frequency-domain feature analysis [16], and time-frequency-domain feature analysis [17] stand out as the most commonly employed methods for feature extraction. Time-domain features are characteristics derived directly from the signal's amplitude with time as the independent variable. Due to their convenience and intuitive computation, time-domain features have gained widespread popularity in sEMG feature extraction [18]. In the time domain, sEMG signals are typically regarded as zero-mean stochastic signals with variance altering according to signal intensity [19]. Prominent time-domain features for surface electromyography signals include root mean square (RMS), zero crossing (ZC), waveform length (WL), variance (VAR), and mean absolute value (MAV). Frequency-domain features, on the other hand, involve spectral or power spectral (PS) features obtained through the application of fast Fourier transform (FFT) to the original sEMG signals, allowing for direct observation of the signal's frequency band distribution. Frequently employed surface electromyography frequency-domain features include PS, mean frequency (MNF), median frequency (MF), frequency ratio (FR), and autoregressive coefficients (AR). The fusion of time-domain and frequency-domain features enables the simultaneous assessment of the temporal and spectral aspects of sEMG signals, providing comprehensive insights into muscle physiological variations. Presently, primary methods for conducting time-frequency analysis of surface electromyography signals encompass short-time Fourier transform (STFT), Wigner-Ville transform (WVT), and wavelet transform. Traditional time-domain and frequency-domain surface electromyography analysis methods only describe time-domain features or only frequency-domain features, which may not fully reflect the characteristic information of sEMG signals. Nevertheless, time-frequency-domain feature analysis methods offer a means to overcome these constraints [20]. Electromyographic (EMG) signals, often characterized by temporal instability, present a unique challenge and opportunity in the domain of human movement analysis, while these signals exhibit temporal variations, they also retain essential spatial information across different sensor channels. Recent advancements in the field, such as the pioneering work by [PERSON] et al. [21], have harnessed this spatial consistency by introducing geometric methodologies that leverage the topological characteristics inherent to EMG signals. Theirapproach emphasizes the fusion of spatial topological structural attributes with traditional time-frequency features. This fusion not only counters temporal feature instability but also significantly enriches the feature information crucial for precise and efficient EMG signal decoding. In the quest for enhancing the decoding of EMG signals, the concept of feature-level fusion has gained prominence, especially in models reliant on multimodal data sources [22]. Traditional methods for analyzing surface EMG (sEMG) signals, often constrained to either temporal or spectral features alone, face limitations. To address this, researchers, such as [PERSON] et al., in their comprehensive review in 2020 [20], proposed an innovative approach incorporating time-frequency-domain feature analysis.