{
  "abstract": "Brain-computer interfaces (BCIs) decode neural activity into commands, yet most existing systems rely on fixed-window decoding that may result in redundant observation or unreliable predictions due to insufficient evidence. Adaptive temporal decision-making (ATDM) addresses this accuracy-time trade-off by progressively accumulating EEG evidence and deciding when to stop. However, existing EEG encoders are mainly designed for fixed-window decoding and may not provide reliable state representations under variable observation lengths. In addition, current ATDM-oriented encoders are typically tailored to specific EEG paradigms, limiting their applicability across different BCI tasks. To address these limitations, we propose ProtoTrigger, a two-stage prototype learning-based EEG state encoder for ATDM. ProtoTrigger uses prototype matching to extract stable local EEG embeddings and prototype-based attention to aggregate decision-relevant temporal evidence during progressive observation. Offline evaluations across three EEG paradigms demonstrated state-of-the-art accuracy-time trade-offs and strong generalizability across different EEG paradigms. An online human-in-the-loop augmented reality-based BCI experiment further demonstrated its real-time feasibility. These results suggest that ProtoTrigger provides a general EEG state encoding framework for efficient ATDM-based BCI systems.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.22088",
  "author_total": 8,
  "authors": [
    {
      "name": "Beining Cao"
    },
    {
      "name": "Ziyi Zhao"
    },
    {
      "name": "Xiaowei Jiang"
    },
    {
      "name": "Daniel Leong"
    },
    {
      "name": "Yingtao Ren"
    },
    {
      "name": "Thomas Do"
    },
    {
      "name": "Yu-Cheng Fred Chang"
    },
    {
      "name": "Chin-Teng Lin"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/agmthcyhs8ftv.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.22088",
  "fulltext_url": null,
  "id": "agmthcyhs8ftv",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.22088",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.HC"
    ],
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "eess.SP"
    ],
    [
      "arxiv",
      "q-bio.NC"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2609.22088",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/agmthcyhs8ftv.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:28Z",
      "native_id": "2609.22088",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.22088"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.NC",
      "subfield": "neuroscience"
    }
  ],
  "title": "Learning Dynamic Neural Evidence Representations for Time-Adaptive Brain-Computer Interfaces",
  "title_text": "Learning Dynamic Neural Evidence Representations for Time-Adaptive Brain-Computer Interfaces",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:28Z",
  "url": "https://research.draconitic.com/paper/agmthcyhs8ftv",
  "venue": "arXiv",
  "versions": []
}