Minimally invasive implantation of scalable high-density cortical microelectrode arrays for multimodal neural decoding and stimulation

High-bandwidth brain-computer interfaces rely on invasive surgical procedures or brain-penetrating electrodes. Here we describe a cortical 1,024-channel thin-film microelectrode array and we demonstrate its minimally invasive surgical delivery that avoids craniotomy in porcine models and cadavers. We show recording and stimulation from the same electrodes to large portions of the cortical surface, and the reversibility of delivering the implants to multiple functional regions of the brain without damaging the cortical surface. We evaluate the performance of the interface for high-density neural recording and visualizing cortical surface activity at spatial and temporal resolutions and total spatial extents. We demonstrate accurate neural decoding of somatosensory, visual and volitional walking activity, and achieve focal neuromodulation through cortical stimulation at sub-millimetre scales. We report the feasibility of intraoperative use of the device in a five-patient pilot clinical study with anaesthetized and awake neurosurgical patients, characterizing the spatial scales at which sensorimotor activity and speech are represented at the cortical surface. The presented neural interface demonstrates the highly scalable nature of micro-electrocorticography and its utility for next-generation brain-computer interfaces.

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Publication Details

Journal
Nature Biomedical Engineering
Published
2025-10-02
DOI
https://doi.org/10.1038/s41551-025-01501-w
Citations
5
Primary Topic
Neuroscience and Neural Engineering
Type
article
Field-Weighted Citation Impact
4.55

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article

Minimally invasive implantation of scalable high-density cortical microelectrode arrays for multimodal neural decoding and stimulation

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5 citations
Nature Biomedical Engineering
Neuroscience and Neural Engineering
4.55
article

Minimally invasive implantation of scalable high-density cortical microelectrode arrays for multimodal neural decoding and stimulation

Elton Ho, Demetrios T. Papageorgiou, Benjamin I. Rapoport, Joshua S. Miller, Mark Hettick, Kyle B. Reed, Vanessa Tolosa, Adam J. Poole, Manuel Merino-Monge, Sanjay Bhatia, Mark Murphy, Morgan LaMarca, Yoon Woo Byun, Peter E. Konrad, Kazutaka Takahashi, Michael Mager, Craig H. Mermel, Timothy Hanson, Kate Gelman, Daniel Trietsch, Sang‐Ho Lee, Stephanie Rider
article en
5 citations

Abstract

High-bandwidth brain-computer interfaces rely on invasive surgical procedures or brain-penetrating electrodes. Here we describe a cortical 1,024-channel thin-film microelectrode array and we demonstrate its minimally invasive surgical delivery that avoids craniotomy in porcine models and cadavers. We show recording and stimulation from the same electrodes to large portions of the cortical surface, and the reversibility of delivering the implants to multiple functional regions of the brain without damaging the cortical surface. We evaluate the performance of the interface for high-density neural recording and visualizing cortical surface activity at spatial and temporal resolutions and total spatial extents. We demonstrate accurate neural decoding of somatosensory, visual and volitional walking activity, and achieve focal neuromodulation through cortical stimulation at sub-millimetre scales. We report the feasibility of intraoperative use of the device in a five-patient pilot clinical study with anaesthetized and awake neurosurgical patients, characterizing the spatial scales at which sensorimotor activity and speech are represented at the cortical surface. The presented neural interface demonstrates the highly scalable nature of micro-electrocorticography and its utility for next-generation brain-computer interfaces.

Nature Biomedical Engineering
West Virginia University (US), Naval Medical Research Command (US), Blanchette Rockefeller Neurosciences Institute (US), Precision for Medicine (United States) (US), Precision Research (United States) (US)
National Science Foundation, West Virginia University
Openalex Percentile: Top 5%
Neuroscience and Neural Engineering
4.55
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