Rethinking EEG Spatial Super-Resolution as Dynamic State Decomposition and Filtering
EEG spatial super-resolution aims to recover high-density neural signals from sparse electrode observations. Existing methods have achieved substantial progress through spatial modeling, generative reconstruction, and temporal representation learning. However, the current EEG state contains two different sources of information: predictable evolution inherited from preceding states and newly emerging changes revealed by the current observation. Most approaches typically do not model these two components explicitly. In this work, we formulate streaming EEG spatial super-resolution as a sequential state filtering problem and propose Dynamic State Filtering (DSF). Rather than relying on a sin...
| Authors | Hongjun Liu, Leyu Zhou, Chang Ge et al. |
|---|---|
| Primary category | cs.AI |
| Published | 17 October 2026 |
| Last updated at source | 17 October 2026 |
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