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Optimal location for gesture decoding in the sensorimotor cortex and implications for brain-computer interface research.

Overview

Authors: Maria Kromm1, Mariana P Branco1,2, Mathijs Raemaekers1,2, Nick F Ramsey1,2
ORCID iDs: Maria Kromm, Nick F Ramsey
  1. University Medical Center Utrecht Brain Center, Department of Neurology and Neurosurgery, Utrecht, the Netherlands
  2. Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
Institutions: Utrecht University (Netherlands); University Medical Center Utrecht (Netherlands); Radboud University Nijmegen (Netherlands); Donders Institute for Brain, Cognition and Behaviour (Netherlands)
Journal: NeuroImage, volume 329, article 121837
Dates: published online 2 March 2026; in print 1 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.neuroimage.2026.121837 · PMID 41780622 · PMCID PMC13249550 · OpenAlex W7133231629
Open access: gold, a free copy (OpenAlex)
Status: code on request
Categories: fMRI (modality), human (organism), systems (subfield)
Methods: Statistics, Machine learning, Preprocessing, Connectivity, fMRI & imaging
Keywords: Brain-computer interfaces, High-field fMRI, Hand gestures, Classification
MeSH: Brain Mapping*, Brain-Computer Interfaces*, Gestures*, Sensorimotor Cortex*, Adult, Female, Hand, Humans, Magnetic Resonance Imaging, Male, Support Vector Machine, Young Adult (* major topic)
Topic: EEG and Brain-Computer Interfaces (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Dutch Research Council (NWO) (OCENW.M22.142, 17619); National Institute of Neurological Disorders and Stroke (UH3NS114439); European Innovation Council; NINDS NIH HHS (UH3 NS114439); Technology Foundation STW (19072)
Citations: not cited yet (Europe PMC); 81 references in the paper

Abstract

Implantable brain-computer interfaces (iBCIs) aim to restore communication in individuals with severe motor impairments. For good iBCI performance, it is important to target an optimal location. In this study, we used high-resolution 7-Tesla functional magnetic resonance imaging (fMRI) to map the spatial distribution of brain activity that can discriminate between a large number of hand gestures. Ten able-bodied participants performed 20 different unimanual hand gestures. Using support vector machines, we measured decodability across the cortex. The highest decoding performance was achieved in the hand region of the sensorimotor cortex. Moreover, we found that a subset of six well-distinguishable gestures could predict the optimal decoding location for the full set, suggesting that a carefully chosen subset can effectively guide pre-implantation mapping. Furthermore, while significant decoding was possible from sulcal as well as gyral regions of the precentral cortex, our analyses revealed that the sulcal area did not contribute unique information beyond that found in adjacent gyral regions. Similarly, decoding in the postcentral cortex was primarily driven by the gyrus. This indicates that surface recordings may suffice for iBCIs. Together, these findings offer practical guidance for future iBCI electrode placement, with the potential to improve communication and autonomy for individuals with severe motor impairments.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

The paper says that its authors' code is available on request: it was not published with the paper, so there is nothing to verify.

The paper's code and data availability statement is in the Data section.

Tracing map

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Data

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Data availability statement

The data and code used during the current study are available from the corresponding author upon reasonable request.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 4 authors, 4 keywords, 12 MeSH terms, 5 funders, 76 references.

Cite

This paper

Kromm, M., Branco, M. P., Raemaekers, M., & Ramsey, N. F. (2026). Optimal location for gesture decoding in the sensorimotor cortex and implications for brain-computer interface research. NeuroImage, 329, 121837. https://doi.org/10.1016/j.neuroimage.2026.121837

BibTeX

@article{kromm2026optimal,
author = {Kromm, Maria and Branco, Mariana P and Raemaekers, Mathijs and Ramsey, Nick F},
title = {{Optimal location for gesture decoding in the sensorimotor cortex and implications for brain-computer interface research}},
journal = {NeuroImage},
year = {2026},
month = mar,
volume = {329},
pages = {121837},
publisher = {Elsevier BV},
issn = {1053-8119},
doi = {10.1016/j.neuroimage.2026.121837},
url = {https://doi.org/10.1016/j.neuroimage.2026.121837},
pmid = {41780622},
pmcid = {PMC13249550}
}

RIS

TY - JOUR
AU - Kromm, Maria
AU - Branco, Mariana P
AU - Raemaekers, Mathijs
AU - Ramsey, Nick F
TI - Optimal location for gesture decoding in the sensorimotor cortex and implications for brain-computer interface research
T2 - NeuroImage
J2 - Neuroimage
PY - 2026
DA - 2026/03/02
VL - 329
SP - 121837
SN - 1053-8119
PB - Elsevier BV
DO - 10.1016/j.neuroimage.2026.121837
UR - https://doi.org/10.1016/j.neuroimage.2026.121837
LA - en
ER -

CSL-JSON

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