## 5 Experiment and Results
### Comparison of Different Feature Extraction Combinations  
To evaluate the performance of the SPS method on different datasets, we selected six publicly available datasets for validation. Additionally, to assess the improvement in EMG pattern recognition achieved through the combination of spatial-domain, frequency-domain, and time-domain features from multiple perspectives, we tested various combinations of feature domains. Furthermore, to mitigate any biases introduced by different classifiers, we conducted tests using four classifiers: LDA, SVM, KNN, and ANN, covering both machine learning and deep learning methods to ensure the generalizability and practicality of our approach. The results presented in Table 3 demonstrate that employing a multi-perspective fusion approach consistently yields superior outcomes across most classifiers, with the deep learning classifier ANN showing the most significant performance improvement. Across all six datasets, the performance of the artificial neural network (ANN) surpasses those of other machine learning classifiers. The utilization of the ICC, TD, and mDWT-fused feature-extraction method exhibits superior performance compared to other feature-extraction approaches, showcasing average classification accuracy improvements of 2.04%, 3.17%, 4.27%, 2.27%, 4.93%, and 4.26% on Ninapro DB4, DB5, BioPatRec DB1-DB3, and Mendelay Data, respectively. Notably, the smallest enhancement is observed in Ninapro DB4, while the most significant improvement is witnessed in BioPatRec DB3. Additionally, Table 3 indicates that the inclusion of multiple feature perspectives leads to performance improvements in most classifiers. In cases where spatial domain features are combined, there is a performance increase of 2-3% compared to methods that lack such features. Moreover, multi-perspective feature classification, when compared to single-feature domain methods, results in enhancements of at least 3% or more. However, it is important to note that SVM and LDA exhibit distinctive trends. The SVM classifier demonstrates greater sensitivity to frequency domain feature information, achieving higher accuracy in feature recognition when compared to multi-perspective fusion methods. It excels at handling feature information in the frequency domain but struggles with multi-dimensional data. In contrast, LDA exhibits a sensitivity to time-domain feature information, performing well in both single-feature domain and time-frequency-domain combination methods. Nevertheless, its capabilities in managing multi-dimensional feature information are comparatively weaker than other classifiers. Figure 4: For the selected KNN classifier, confusion matrices were generated to evaluate the classification performance with and without the involvement of SPS in the methods ICC, TD, and mDWT. The dataset used for this evaluation was BioPatRec DB1. (**a**) The confusion matrix with the inclusion of the SPS method. (**b**) The confusion matrix without the SPS method. The symbol \(\dagger\) indicates an SPS operation that extends the sEMG signal. Furthermore, we assessed F1-score performance on these datasets. The F1-score is employed to characterize performance in scenarios with sample imbalances for each label, as illustrated in Figure 5. We observed that classifiers with stronger decoding capabilities could extract more information from electromyographic signals as the feature information increased. Even classifiers with relatively weaker decoding abilities exhibited good performance. Importantly, the ANN classifier demonstrated performance improvements after the fusion of spatial domain features, further enhancing the multidimensional perspective.