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<p>Humans naturally excel at selective auditory attention, yet this ability is often impaired in individuals with hearing loss. Electroencephalography-based brain–computer interfaces (BCIs) can capture auditory task-evoked neural responses and assess attention states through brainwave analysis. However, traditional BCI systems rely on full-scalp wet electrodes and computation-heavy algorithms, limiting wearability and energy efficiency. Here we present a synergistic material–algorithm framework that combines an oxidant-free, room-temperature self-polymerization strategy to fabricate oligo(3,4-ethylenedioxythiophene)-based zwitterionic hydrogel electrodes with SDBformer, an ultra-lightweight, energy-efficient spiking Transformer algorithm. The hydrogel electrodes exhibit low on-skin impedance and stable in vivo electrocorticography for high-accuracy BCI control using steady-state visual evoked potentials, while SDBformer achieves robust auditory attention decoding using only eight temporal electroencephalography channels, matching the performance of full-scalp wet recordings. This integrated approach advances the development of practical, low-power wearable BCIs for neurotechnology applications in auditory attention and assistive hearing.</p>

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Synergistic material–algorithm design enables robust auditory attention decoding brain–computer interface applications

  • 期刊论文

  • 发表日期：

    2026年7月20日

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  • Yuzhe Gu,
  • Yuan Liao,
  • Yang Li,
  • Jingyu He,
  • Haoxuan Yu,
  • Guansheng Xing,
  • Xiaotian Wang,
  • Aoxi Yu,
  • Qianhe Shu,
  • Liya Huang,
  • Shujuan Liu,
  • Keqiang Gao,
  • Wei Huang,
  • Qiang Zhao

摘要

Humans naturally excel at selective auditory attention, yet this ability is often impaired in individuals with hearing loss. Electroencephalography-based brain–computer interfaces (BCIs) can capture auditory task-evoked neural responses and assess attention states through brainwave analysis. However, traditional BCI systems rely on full-scalp wet electrodes and computation-heavy algorithms, limiting wearability and energy efficiency. Here we present a synergistic material–algorithm framework that combines an oxidant-free, room-temperature self-polymerization strategy to fabricate oligo(3,4-ethylenedioxythiophene)-based zwitterionic hydrogel electrodes with SDBformer, an ultra-lightweight, energy-efficient spiking Transformer algorithm. The hydrogel electrodes exhibit low on-skin impedance and stable in vivo electrocorticography for high-accuracy BCI control using steady-state visual evoked potentials, while SDBformer achieves robust auditory attention decoding using only eight temporal electroencephalography channels, matching the performance of full-scalp wet recordings. This integrated approach advances the development of practical, low-power wearable BCIs for neurotechnology applications in auditory attention and assistive hearing.