A wireless subdural-contained brain–computer interface with 65,536 electrodes and 1,024 channels

18 min read Original article ↗

Data availability

All electrophysiological data relevant to the figures presented in this paper are available via GitHub at https://github.com/klshepard/bisc with a version archived in Zenodo (https://doi.org/10.5281/zenodo.17074065)70. All other relevant data are available from the corresponding authors upon reasonable request.

Code availability

All scripts used for the data analysis are available via GitHub at https://github.com/klshepard/bisc. All other relevant codes are available from the corresponding authors upon reasonable request.

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Acknowledgements

This work was partly supported by the Defense Advanced Research Project Agency (DARPA) under contract number N66001-17-C-4001, the Department of the Defense Congressionally Directed Medical Research Program under contract number HT9425-23-1-0758, the National Science Foundation under grant number 1546296 and the National Institutes of Health under grant number R01DC019498. We acknowledge the use of facilities and instrumentation at the Columbia Nano Initiative, the CUNY ASRC and the UPenn Quattrone Nanofabrication Facility. We also thank Y. Borisenkov, A. Banees and K. Kim at Columbia University for help with chip processing and many helpful discussions.

Author information

Author notes

  1. These authors contributed equally: Taesung Jung, Nanyu Zeng.

Authors and Affiliations

  1. Department of Electrical Engineering, Columbia University, New York, NY, USA

    Taesung Jung, Nanyu Zeng, Jason D. Fabbri, Rizwan Huq, Mohit Sharma, Yaoxing Hu, Girish Ramakrishnan, Kevin Tien, Abhinav Parihar, Heyu Yin, Ilke Uguz & Kenneth L. Shepard

  2. Kampto Neurotech LLC, Troy, NY, USA

    Nanyu Zeng

  3. Department of Computer Science, Columbia University, New York, NY, USA

    Guy Eichler, Paolo Mantovani, Alexander Misdorp & Luca P. Carloni

  4. Department of Ophthalmology, Byers Eye Institute, Stanford University, Stanford, CA, USA

    Zhe Li, Konstantin Willeke, Gabrielle J. Rodriguez, Cate Nealley, Saumil Patel & Andreas Tolias

  5. Stanford Bio-X, Stanford University, Stanford, CA, USA

    Zhe Li, Konstantin Willeke, Gabrielle J. Rodriguez, Cate Nealley, Sophia Sanborn, Saumil Patel & Andreas Tolias

  6. Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA

    Zhe Li, Konstantin Willeke, Gabrielle J. Rodriguez, Cate Nealley, Sophia Sanborn, Saumil Patel & Andreas Tolias

  7. Department of Biomedical Engineering, Columbia University, New York, NY, USA

    Erfan Zabeh, Anup Das & Kenneth L. Shepard

  8. Institute of Computer Science and Campus Institute Data Science, University of Göttingen, Göttingen, Germany

    Konstantin Willeke

  9. Center for Neural Science, New York University, New York, NY, USA

    Katie E. Wingel, Agrita Dubey & Bijan Pesaran

  10. Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, USA

    Katie E. Wingel, Agrita Dubey, Denise Oswalt, Daniel Yoshor & Bijan Pesaran

  11. Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, USA

    Denise Oswalt & Bijan Pesaran

  12. Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA

    Denise Oswalt & Bijan Pesaran

  13. Center for Neuroscience and Artificial Intelligence, Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA

    Tori Shinn & Andreas Tolias

  14. Neuroscience Research Institute, University of California Santa Barbara, Santa Barbara, CA, USA

    Tjitse van der Molen & Kenneth S. Kosik

  15. Department of Molecular, Cellular and Developmental Biology, University of California Santa Barbara, Santa Barbara, CA, USA

    Tjitse van der Molen & Kenneth S. Kosik

  16. Department of Neurological Surgery, Columbia University, New York, NY, USA

    Ian Gonzales, Eleonora Spinazzi, Brett Youngerman & Kenneth L. Shepard

  17. Department of Applied Physics, Caltech, Pasadena, CA, USA

    Michael Roukes

  18. Department of Physics, Caltech, Pasadena, CA, USA

    Michael Roukes

  19. Department of Bioengineering, Caltech, Pasadena, CA, USA

    Michael Roukes

  20. Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA, USA

    Jeffrey Knecht

  21. Department of Pathology and Cell Biology, Columbia University, New York, NY, USA

    Peter Canoll

  22. Department of Neurology and Neuroscience Institute, University of Chicago, Chicago, IL, USA

    Joshua Jacobs

  23. Shirley Ryan Ability Labs, Chicago, IL, USA

    R. James Cotton

  24. Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, USA

    R. James Cotton

  25. Department of Electrical Engineering, Stanford University, Stanford, CA, USA

    Andreas Tolias

Authors

  1. Taesung Jung
  2. Nanyu Zeng
  3. Jason D. Fabbri
  4. Guy Eichler
  5. Zhe Li
  6. Erfan Zabeh
  7. Anup Das
  8. Konstantin Willeke
  9. Katie E. Wingel
  10. Agrita Dubey
  11. Rizwan Huq
  12. Mohit Sharma
  13. Yaoxing Hu
  14. Girish Ramakrishnan
  15. Kevin Tien
  16. Paolo Mantovani
  17. Abhinav Parihar
  18. Heyu Yin
  19. Denise Oswalt
  20. Alexander Misdorp
  21. Ilke Uguz
  22. Tori Shinn
  23. Gabrielle J. Rodriguez
  24. Cate Nealley
  25. Tjitse van der Molen
  26. Sophia Sanborn
  27. Ian Gonzales
  28. Michael Roukes
  29. Jeffrey Knecht
  30. Kenneth S. Kosik
  31. Daniel Yoshor
  32. Peter Canoll
  33. Eleonora Spinazzi
  34. Luca P. Carloni
  35. Bijan Pesaran
  36. Saumil Patel
  37. Joshua Jacobs
  38. Brett Youngerman
  39. R. James Cotton
  40. Andreas Tolias
  41. Kenneth L. Shepard

Contributions

K.L.S., N.Z., T.J. and R.J.C. conceived the project. N.Z., T.J., G.E., M.S., K.T., G.R., Y.H., K.L.S. and R.J.C. designed the implant circuitry. J.D.F., J.K. and H.Y. post-processed the implant. N.Z. and T.J. implemented the relay station hardware. G.E., N.Z., P.M., R.J.C., S.P., T.J., A.M. and L.P.C. implemented the relay station software. T.J., N.Z., J.D.F. and S.P. performed the bench-top characterizations. B.Y., E.S., T.J., N.Z., K.L.S., R.H., I.G. and G.E. performed the in vivo experiments on the porcine subject. T.J., B.Y. and P.C. conducted the porcine data analysis and histology. B.P., A. Dubey, K.E.W., N.Z. and T.J. performed the in vivo experiments on the motor cortex of the NHP. T.J., B.P. and K.E.W. performed the motor cortex data analysis. A.T., S.P., K.L.S., R.J.C., T.J., N.Z., G.E., T.S., G.J.R. and C.N. performed the in vivo experiments on the visual cortex of the NHP. Z.L., K.W., A.T., S.P., D.O., R.J.C., E.Z., A. Das and J.J. performed the visual cortex data analysis. K.L.S., A.T., B.P., M.R., J.J. and D.Y. acquired the funding. K.L.S., A.T., B.Y., B.P., R.J.C., L.P.C. and J.J. provided supervision. T.J., N.Z., J.D.F., G.E., K.L.S., Z.L., K.W., A.T., A. Das, E.Z., J.J. and S.P. wrote the paper with review and editing contributed by all authors.

Corresponding authors

Correspondence to Andreas Tolias or Kenneth L. Shepard.

Ethics declarations

Competing interests

N.Z. is a principal with Kampto Neurotech, LLC, which is commercializing the BISC technology. The BISC technology is patented under US patent 11617890, issued on 4 April 2023, and exclusively licensed to Kampto from Columbia University. The other authors declare no competing interests.

Peer review

Peer review information

Nature Electronics thanks the anonymous reviewers for their contribution to the peer review of this work.

Additional information

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Extended data

Extended Data Fig. 1 Bench-top in vitro characterization of the BISC implant.

(a) Electrochemical impedance spectroscopy of titanium nitride electrode. (b) Frequency response across different gain configurations from a representative 16×16 recording. Note that gain is programmed through a single back-end amplifier that is shared by all pixels. Error bars indicate standard error (SE), and dashed rectangle marks the effects of boxcar sampling (flat band gains: 53.7 ± 0.20 dB, 57.2 ± 0.21 dB, 60.7 ± 0.20 dB, 64.2 ± 0.19 dB, values: mean ± SD. n = 255, 255, 245, 235). (c) Histogram of normalized channel gain variation for each recording mode (16×16 mode: 100 ± 5.1%, 32×32 mode: 100 ± 4.8%, values: mean ± SD. n = 15,163 and 62,245). (d) Frequency response across different high-pass (HP) filter configurations from a representative 16×16 recording. Error bars indicate SE (3-dB corner: 4.19 ± 2.28 Hz, 13.30 ± 2.37 Hz, 54.42 ± 1.98 Hz, values: mean ± SD. n = 244, 254, 256). (e) Input-referred noise (IRN) spectrum averaged over representative pixels (n = 10) for each recording mode. (f) Histogram of channel IRN for each recording mode, integrated from 10 Hz to 4 kHz (16×16 mode: 7.68 ± 3.11 μVRMS, 32×32 mode: 16.51 ± 6.85 μVRMS, values: mean ± SD. n = 15,163 and 62,245).

Extended Data Fig. 2 BISC recordings over visual cortex with natural images.

(a) We presented static colored natural images, while the monkey maintained fixation (120 ms presentation time per image, 15 images per trial, 1200 ms inter-trial period). Each image (10°×10°) was centered 3° to the right and below the fixation spot. (b) Model architecture: Pre-processed stimuli (184 × 184 pixels) and neuronal responses were used to train a neural predictive model, which takes images as an input and outputs an estimate of the underlying neuronal activity. We passed the images through a ConvNext model, pre-trained on an image classification task to obtain image embeddings, that is a shared feature space. We then computed the neuronal responses by passing the feature activations through a transformer-based readout followed by a non-linearity stage. (c) Explainable variance, a measure of response reliability to natural images, plotted against the model’s predictive performance (correlation between prediction and average neural response to repeated presentations) of all 144 channels (explainable variance 0.24 ± 0.09, and correlation to average 0.69 ± 0.14. values: mean ± SD). Only channels with an explainable variance greater than or equal to 0.1 are included in these analyses. (d) Spatial map of explainable variance across the recording array (same layout as in Fig. 4 and Fig. 5e). (e) Same as (d), but showing the model’s predictive performance (correlation to average neural response). (f) Schematic illustrating optimization of maximally exciting images (MEIs). A random starting image was iteratively optimized to elicit maximal activity for each in-silico channel, revealing the visual features to which that channel is selective. Three example MEIs from areas V1, V2, and V4 are shown. (g) MEIs for all 144 channels across the array which reliably responded to repeated image presentations. MEIs in area V1 are characterized by oriented Gabor filters, while the channels overlying area V2 and V4 exhibit more complex, color opponent feature tuning. Credit: cow image in a,b, Nicolas Vigier, flickr under a Creative Commons license CC0.

Supplementary information

Supplementary Information

Supplementary Discussions 1–10, Figs. 1–26, Tables 1 and 2, and captions for Videos 1–9.

Reporting Summary

Supplementary Video 1

Normalized somatosensory evoked potential (SSEP) recording from a porcine model, trial averaged (n = 100 per location).

Supplementary Video 2

Motor cortex recording from a NHP model performing asynchronous reach-and-grab task.

Supplementary Video 3

Dot-triggered-average responses of all channels without filtering.

Supplementary Video 4

Dot-triggered-average responses of all channels after wavelet transformation (central frequency 8 Hz).

Supplementary Video 5

Dot-triggered-average responses of all channels after wavelet transformation (central frequency 16 Hz).

Supplementary Video 6

Dot-triggered-average responses of all channels after wavelet transformation (central frequency 32 Hz).

Supplementary Video 7

Dot-triggered-average responses of all channels after wavelet transformation (central frequency 64 Hz).

Supplementary Video 8

Dot-triggered-average responses of all channels after wavelet transformation (central frequency 128 Hz).

Supplementary Video 9

Dot-triggered travelling waves used for decoding stimuli location. The travelling waves are computed from the γ-band (30–90 Hz) signals recorded from 32 × 32 spatially dense channels at a pitch of 26.5 μm × 29 μm. The spatiotemporal sequence of these travelling waves, measured within each dot presentation, is used to predict the current location of the dot stimuli presented to the subject.

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Jung, T., Zeng, N., Fabbri, J.D. et al. A wireless subdural-contained brain–computer interface with 65,536 electrodes and 1,024 channels. Nat Electron 8, 1272–1288 (2025). https://doi.org/10.1038/s41928-025-01509-9

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