S 2 M-Former: Spiking Symmetric Mixing Branchformer for Brain Auditory Attention Detection

Han Zhang, Jiaqi Wang, Yi Zhong, Zhengyu Ma, Chenlin Zhou, Xiongri Shen, Leilei Zhao, Siqi Cai, Zhenxi Song, Zhiguo Zhang

arXiv · PDF · Open in the Atlas

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• JackieWang9811/S2M-Former

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Abstract

Auditory attention detection (AAD) aims to decode listeners' focus in complex auditory environments from electroencephalography (EEG) recordings, which is crucial for developing neuro-steered hearing devices. Despite recent advancements, EEG-based AAD remains hindered by the absence of synergistic frameworks that can fully leverage complementary EEG features under energy-efficiency constraints. We propose S 2 M-Former, a novel spiking symmetric mixing framework to address this limitation through two key innovations: i) Presenting a spike-driven symmetric architecture composed of parallel spatial and frequency branches with mirrored modular design, leveraging biologically plausible token-channel mixers to enhance complementary learning across branches; ii) Introducing lightweight 1D token sequences to replace conventional 3D operations, reducing parameters by 14.7×. The brain-inspired spiking architecture further reduces power consumption, achieving a 5.8× energy reduction compared to recent ANN methods, while also surpassing existing SNN baselines in terms of parameter efficiency and performance. Comprehensive experiments on three AAD benchmarks (KUL, DTU and AV-GC-AAD) across three settings (within-trial, cross-trial and cross-subject) demonstrate that S 2 M-Former achieves comparable state-of-the-art (SOTA) decoding accuracy, making it a promising low-power, high-performance solution for AAD tasks. Code is available at https://github.com/JackieWang9811/S2M-Former.

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