## 7 Conclusions  
To better decode additional feature information from electromyographic signals, thereby achieving the recognition task of movement postures, this paper introduces a method for EMG pattern recognition using space-to-space (SPS), which integrates multiple feature perspectives from the spatial domain (ICC), time domain, and frequency domain. By combining spatially-extended SPS with subsequent feature extraction techniques, it extensively explores spatial, temporal, and frequency-domain features within and across different channels of electromyographic signals. To evaluate the performance of the proposed architecture, experiments were conducted on six sEMG benchmark databases: Ninapro DB4, Ninapro DB5, BioPatRec DB1, BioPatRec DB2, BioPatRec DB3, and Mendeley Data. The results demonstrate that the feature-extraction method based on multi-perspective fusion can better extract kinematic information from sEMG signals and decode them, leading to improved recognition accuracy compared to traditional feature-extraction methods. The experimental findings underscore the superiority of our approach and offer a novel method for electromyographic pattern recognition.  
In the future, we plan to explore alternative feature-extraction methods in Euclidean space, projecting them into Riemannian space to seek more suitable representations of electromyographic signals. Additionally, we aim to enhance the algorithm's robustness and stability by combining these methods with existing feature extraction techniques. Furthermore, we will investigate the fusion of modal information from other domains with electromyographic signals and establish a unified decoding framework, providing a new option for human-computer interaction.  
Conceptualization, W.C. and Y.N.; Formal analysis, B.X.; Methodology, W.C. and Y.N.; Validation, Z.G. and S.H.; Writing--original draft, W.C. and Y.N.; Writing--review and editing, B.X. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant 62073271, the Natural Science Foundation of Fujian Province (2020)01890, 2020)01891), and the Scientific Fund Projects in Fujian University of Technology (GY-Z17144), and in part by the Scientific Research Projects of the Science and Technology Department in Fujian of China (2020)01890, 202310024), the Provincial Project of Education Department in Fujian of China (JT180344, FBJY20230078, FJJKBK23-045), and the Scientific Research Projects in Fujian University of Technology (KF-X19002, KF-19-22001, GY-H-21191, GY-Z21049, GY-Z220210).  
Not applicable.  
The data presented in this study are openly available in public domain at [[https://ninapro.hevs.ch](https://ninapro.hevs.ch)]([https://ninapro.hevs.ch](https://ninapro.hevs.ch)), [[https://github.com/biopatrec/biopatrec](https://github.com/biopatrec/biopatrec)]([https://github.com/biopatrec/biopatrec](https://github.com/biopatrec/biopatrec)) and [[https://data.mendeley.com/datasets/ckwc76](https://data.mendeley.com/datasets/ckwc76) wrz2/2]([https://data.mendeley.com/datasets/ckwc76](https://data.mendeley.com/datasets/ckwc76) wrz2/2) (accessed on 4 November 2023).  
The authors declare no conflict of interest.