Abstract:Spatio-temporal signals arising from event-driven biological processes, such as surface electromyography (sEMG), exhibit asynchronous and highly structured activation patterns that are challenging to model using conventional discrete or purely real-valued representations.
In this work, we propose a continuous temporal modeling framework based on interference-based wave representations. The approach maps event-like input signals into a complex-valued latent wave field, where temporal structure is encoded through phase modulation and interactions between latent components.
By projecting the resulting wave field onto an energy domain, the model induces structured activation patterns that capture both temporal localization and relational dependencies within finite observation windows, without relying on explicit recurrence or causal state propagation.
The proposed formulation is particularly suited for event-driven biosignals, where continuous representations enable efficient gradient-based optimization and robust feature extraction. In particular, the method is designed to support learning from sEMG data for downstream control tasks in biomechanical systems, such as prosthetic devices and exoskeletons.
Experimental results demonstrate that the proposed interference-based wave model provides improved representation quality compared to purely real-valued representations, while maintaining computational efficiency suitable for practical deployment.
| Comments: | 18 pages, 3 figures, Submitted to Journal |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.01270 [cs.LG] |
| (or arXiv:2605.01270v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.01270 arXiv-issued DOI via DataCite (pending registration) |
From: Magnus Bengtsson [view email]
[v1]
Sat, 2 May 2026 06:04:55 UTC (2,003 KB)
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