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Human-centred physical neuromorphics with visual brain-computer interfaces

Author

Listed:
  • Gao Wang

    (University of Glasgow)

  • Giulia Marcucci

    (University of Glasgow)

  • Benjamin Peters

    (University of Glasgow)

  • Maria Chiara Braidotti

    (University of Glasgow)

  • Lars Muckli

    (University of Glasgow)

  • Daniele Faccio

    (University of Glasgow)

Abstract

Steady-state visual evoked potentials (SSVEPs) are widely used for brain-computer interfaces (BCIs) as they provide a stable and efficient means to connect the computer to the brain with a simple flickering light. Previous studies focused on low-density frequency division multiplexing techniques, i.e. typically employing one or two light-modulation frequencies during a single flickering light stimulation. Here we show that it is possible to encode information in SSVEPs excited by high-density frequency division multiplexing, involving hundreds of frequencies. We then demonstrate the ability to transmit entire images from the computer to the brain/EEG read-out in relatively short times. High-density frequency multiplexing also allows to implement a photonic neural network utilizing SSVEPs, that is applied to simple classification tasks and exhibits promising scalability properties by connecting multiple brains in series. Our findings open up new possibilities for the field of neural interfaces, holding potential for various applications, including assistive technologies and cognitive enhancements, to further improve human-machine interactions.

Suggested Citation

  • Gao Wang & Giulia Marcucci & Benjamin Peters & Maria Chiara Braidotti & Lars Muckli & Daniele Faccio, 2024. "Human-centred physical neuromorphics with visual brain-computer interfaces," Nature Communications, Nature, vol. 15(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-50775-2
    DOI: 10.1038/s41467-024-50775-2
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    References listed on IDEAS

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    1. Logan G. Wright & Tatsuhiro Onodera & Martin M. Stein & Tianyu Wang & Darren T. Schachter & Zoey Hu & Peter L. McMahon, 2022. "Deep physical neural networks trained with backpropagation," Nature, Nature, vol. 601(7894), pages 549-555, January.
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