{
  "abstract": "Imagined speech decoding from EEG offers a promising route toward naturalistic brain-computer interfaces. Traditional closed-set classifiers treat semantic categories as independent labels and do not explicitly exploit relationships among linguistic intents, limiting output flexibility. We adapt a multimodal contrastive learning framework to imagined speech, aligning EEG trials with language representations in the embedding space of a frozen pretrained text encoder. An EEG encoder is trained with a composite objective combining semantically reweighted EEG-sentence alignment and text-prototype-based class discrimination, encouraging semantic structure while preserving separability among 39 intent categories. We evaluate the approach on the Chisco imagined speech dataset across five subjects, reporting Top-k accuracy, macro-F1, and balanced pairwise matching accuracy (2v2). We also investigate maximum EEG-text cosine similarity (MaxCosine) as an intrinsic indicator of decoding reliability and probe semantic structure beyond category labels using five evaluation-time semantic axes and a qualitative Semantic Gallery Probe (SGP) with previously unseen candidate sentences. The contrastive framework achieved decoding performance comparable to a task-optimized closed-set classifier while mapping EEG into a frozen language embedding space. MaxCosine identified subsets of held-out trials with higher Top-1 and Top-5 accuracy across subjects. Evaluation-time semantic axes showed mean agreement above the 50% chance reference across all five dimensions, and the SGP qualitatively illustrated direct comparison with unseen candidate sentences. Together, these findings suggest that EEG-language alignment can retain comparable fixed-intent decoding while supporting similarity-based retrieval, intrinsic reliability ranking, and evaluation of broader semantic structure.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.20766",
  "author_total": 4,
  "authors": [
    {
      "name": "Shahryar G. Namdari"
    },
    {
      "name": "Francesco Iacomi"
    },
    {
      "name": "Riccardo Barbieri"
    },
    {
      "name": "Luca Mainardi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/emfsz8b6vs0d8.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.20766",
  "fulltext_url": null,
  "id": "emfsz8b6vs0d8",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.20766",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "q-bio.NC"
    ],
    [
      "openalex",
      "2805|EEG and Brain-Computer Interfaces"
    ]
  ],
  "openalex": "W7222598668",
  "pdf_url": "https://arxiv.org/pdf/2610.20766",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-14",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/emfsz8b6vs0d8.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:55Z",
      "native_id": "2610.20766",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.20766"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.NC",
      "subfield": "neuroscience"
    }
  ],
  "title": "Semantic Alignment of EEG and Text: A Contrastive Learning Framework for Decoding Imagined Speech",
  "title_text": "Semantic Alignment of EEG and Text: A Contrastive Learning Framework for Decoding Imagined Speech",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:55Z",
  "url": "https://research.draconitic.com/paper/emfsz8b6vs0d8",
  "venue": "arXiv",
  "versions": []
}