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. An oxidant-free oligomer-based zwitterionic hydrogel electrode and an ultra-lightweight spiking transformer algorithm enable high-fidelity, low-power auditory attention decoding for practical wearable brain–computer interface applications.
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