NeuroNarrator Translates EEG Signals Into Narratives
On Feb 24, 2026, researchers led by Guoan Wang present NeuroNarrator, a generalist EEG-to-text foundation model trained on NeuroCorpus-160K pairing over 160,000 EEG segments with structured clinical descriptions. The architecture aligns temporal waveforms with spatial topographic maps via a contrastive objective and conditions a large language model using a state-space-inspired temporal-spectral context. Evaluations report strong performance across diverse benchmarks and zero-shot transfer tasks.
Key Points
- 1Introduces NeuroNarrator trained on NeuroCorpus-160K, pairing over 160,000 EEG segments with clinical descriptions
- 2Establishes spectro-spatial grounding via contrastive alignment of waveforms and topographic maps for better representations
- 3Enables coherent clinical narrative generation by conditioning an LLM with state-space-inspired temporal and spectral context
Scoring Rationale
Novel EEG-to-text foundation and large dataset drive score, limited by preprint status and niche clinical scope.
Sources
Public references used for this report.
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