{
  "abstract": "Motor imagery (MI) brain-computer interfaces (BCIs) are increasingly investigated for promoting motor recovery after stroke. However, it remains unclear how BCI control improves through repeated training in stroke patients and whether the timing of MI decoding systematically influences this trajectory. To address this question, we implemented a single-case experimental design involving five chronic stroke patients enrolling in a longitudinal electroencephalography MI-BCI training involving their affected hand. Across 18 sessions conducted over six weeks, the onset of a fixed-duration MI decoding window was randomized to occur 1, 2, or 3~s after cue onset according to an alternating-treatments single-case design. Notably, the BCI combined spectral power and functional connectivity features to classify MI states. The MI classification timing had no significant effect on BCI accuracy, fatigue, or spasticity. BCI accuracy increased across training in four of five patients and was accompanied by patient-specific longitudinal changes in local spectral activity and functional connectivity. These convergent trajectories were consistent with acquisition of BCI control, but were not accompanied by clinically meaningful improvement in motor function. Varying the onset of the MI decoding window provided no systematic benefit for BCI control. Instead, differences in neurophysiological trajectories across patients indicate that MI decoding parameters need to be personalized and reassessed throughout the training. Overall, these findings inform more individualized and effective BCI settings for neuromotor stroke rehabilitation.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 13,
  "authors": [
    {
      "name": "Camile Bousfiha"
    },
    {
      "name": "Camille Gabot"
    },
    {
      "name": "Diana Baili"
    },
    {
      "name": "Erin Soller"
    },
    {
      "name": "Marion Couton"
    },
    {
      "name": "Grace Adwane"
    },
    {
      "name": "Michaël Obadia"
    },
    {
      "name": "Chiara Zavanone"
    },
    {
      "name": "Sophie Dupont"
    },
    {
      "name": "Eléonore Bayen"
    },
    {
      "name": "Laurent Bougrain"
    },
    {
      "name": "Paolo Bartolomeo"
    },
    {
      "name": "Fabrizio de Vico Fallani"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/19s6yg9s68176.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.21.26366015",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.10.21.26366015v1.full",
  "id": "19s6yg9s68176",
  "issns": [],
  "landing_url": "https://inria.hal.science/hal-05837840v1",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "hal",
      "0.sdv"
    ],
    [
      "hal",
      "0.spi"
    ],
    [
      "hal",
      "1.sdv.spee"
    ],
    [
      "medrxiv",
      "rehabilitation medicine and physical therapy"
    ],
    [
      "openalex",
      "2805|EEG and Brain-Computer Interfaces"
    ]
  ],
  "openalex": "W7223322818",
  "pdf_url": "https://inria.hal.science/hal-05837840/document",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-21",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/19s6yg9s68176.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:01Z",
      "native_id": "hal-05837840",
      "source": "hal",
      "url": "https://inria.hal.science/hal-05837840v1"
    },
    {
      "first_seen": "2026-10-30T12:21:33Z",
      "native_id": "10.64898/2026.10.21.26366015",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.10.21.26366015v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "hal:0.sdv",
      "subfield": "general"
    },
    {
      "field": "medicine",
      "origin": "hal:1.sdv.spee",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Single-Case Design for Network-based BCI Rehabilitation: Individual Learning Trajectories and Effects of Motor Imagery Timing in Stroke Patients",
  "title_text": "Single-Case Design for Network-based BCI Rehabilitation: Individual Learning Trajectories and Effects of Motor Imagery Timing in Stroke Patients",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:33Z",
  "url": "https://research.draconitic.com/paper/19s6yg9s68176",
  "venue": "HAL",
  "versions": []
}