What the LLM Should Not Say: Boundary-Aware Context Grounding for A Seven-Channel EEG Agent
Large language models (LLMs) can make scientific software easier to use. However, a general model does not automatically know which measurements a particular sensor can support, which algorithms are implemented in the current software, or which conclusions are justified by a computed result. These distinctions are especially important for low-channel electroencephalography (EEG), where sparse spatial coverage and variable signal quality make plausible but unsupported interpretations easy to produce. We present NeuraDock Agent, an open-source architecture that separates a deterministic local EEG engine from a hardware-aware language layer. The numerical engine parses recordings, performs q...
| Authors | Zhiyuan Xu, Yueqing Dai, Junling Li et al. |
|---|---|
| Primary category | cs.AI |
| Published | 25 June 2026 |
| Last updated at source | 26 June 2026 |
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