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Despite this potential, prevailing computational approaches to EEG analysis remain largely confined to task-specific classification objectives or coarse-grained pattern recognition, offering limited support for clinically meaningful interpretation. To address these limitations, we introduce NeuroNarrator, the first generalist EEG-to-text foundation model designed to translate electrophysiological segments into precise clinical narratives. A cornerstone of this framework is the curation of NeuroCorpus-160K, the first harmonized largescale resource pairing over 160,000 EEG segments with structured, clinically grounded natural-language descriptions. Our architecture first aligns temporal EEG waveforms with spatial topographic maps via a rigorous contrastive objective, establishing spectro\u2013spatially grounded representations. Building on this grounding, we condition a Large Language Model through a state-space\u2013inspired formulation that integrates historical temporal and spectral context to support coherent clinical narrative generation. This approach establishes a principled bridge between continuous signal dynamics and discrete clinical language, enabling interpretable narrative generation that facilitates expert interpretation and supports clinical reporting workflows. Extensive evaluations across diverse benchmarks and zero-shot transfer tasks highlight NeuroNarrator\u2019s capacity to integrate temporal, spectral, and spatial dynamics, positioning it as a foundational framework for time\u2013frequency\u2013aware, open-ended clinical interpretation of electrophysiological data.<\/jats:p>","DOI":"10.64898\/2026.03.07.707799","type":"posted-content","created":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T22:15:13Z","timestamp":1773180913000},"source":"Crossref","is-referenced-by-count":0,"title":["NeuroNarrator: A Generalist EEG-to-Text Foundation Model for Clinical Interpretation via Spectro-Spatial Grounding and Temporal State-Space Reasoning"],"prefix":"10.64898","author":[{"given":"Guoan","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Systems Engineering, Stevens Institute of Technology, 1 Castle Point Terrace, Hoboken, 07030, New Jersey, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shihao","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Systems Engineering, Stevens Institute of Technology, 1 Castle Point Terrace, Hoboken, 07030, New Jersey, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun-en","family":"Ding","sequence":"additional","affiliation":[{"name":"Department of Systems Engineering, Stevens Institute of Technology, 1 Castle Point Terrace, Hoboken, 07030, New Jersey, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Systems Engineering, Stevens Institute of Technology, 1 Castle Point Terrace, Hoboken, 07030, New Jersey, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5225-8199","authenticated-orcid":false,"given":"Feng","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Systems Engineering, Stevens Institute of Technology, 1 Castle Point Terrace, Hoboken, 07030, New Jersey, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"key":"2026031300401046000_2026.03.07.707799v1.1","doi-asserted-by":"crossref","first-page":"325","DOI":"10.3389\/fneur.2019.00325","article-title":"EEG source imaging: a practical review of the analysis steps","volume":"10","year":"2019","journal-title":"Frontiers in Neurology"},{"key":"2026031300401046000_2026.03.07.707799v1.2","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-bioeng-062117-120853"},{"key":"2026031300401046000_2026.03.07.707799v1.3","doi-asserted-by":"publisher","DOI":"10.1038\/nature06976"},{"key":"2026031300401046000_2026.03.07.707799v1.4","doi-asserted-by":"publisher","DOI":"10.1038\/nn.4504"},{"key":"2026031300401046000_2026.03.07.707799v1.5","doi-asserted-by":"publisher","DOI":"10.1038\/s41593-018-0108-2"},{"key":"2026031300401046000_2026.03.07.707799v1.6","doi-asserted-by":"publisher","DOI":"10.1145\/1897816.1897838"},{"key":"2026031300401046000_2026.03.07.707799v1.7","doi-asserted-by":"crossref","first-page":"101702","DOI":"10.1016\/j.bspc.2019.101702","article-title":"A review of feature extraction and performance evaluation in epileptic seizure detection using EEG","volume":"57","year":"2020","journal-title":"Biomedical Signal Processing and Control"},{"key":"2026031300401046000_2026.03.07.707799v1.8","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1016\/j.future.2019.06.027","article-title":"Deep learning for EEG motor imagery classification based on multi-layer cnns feature fusion","volume":"101","year":"2019","journal-title":"Future Generation Computer Systems"},{"key":"2026031300401046000_2026.03.07.707799v1.9","doi-asserted-by":"crossref","unstructured":"Supratak, A. , Dong, H. , Wu, C. , Guo, Y. : Deepsleepnet: A model for automatic sleep stage scoring based on raw single-channel EEG. 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