{"ID":2885097,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.05164","arxiv_id":"2508.05164","title":"S$^2$M-Former: Spiking Symmetric Mixing Branchformer for Brain Auditory Attention Detection","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$\\times$. The brain-inspired spiking architecture further reduces power consumption, achieving a 5.8$\\times$ 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.","short_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 f...","url_abs":"https://arxiv.org/abs/2508.05164","url_pdf":"https://arxiv.org/pdf/2508.05164v2","authors":"[\"Jiaqi Wang\",\"Zhengyu Ma\",\"Xiongri Shen\",\"Chenlin Zhou\",\"Leilei Zhao\",\"Han Zhang\",\"Yi Zhong\",\"Siqi Cai\",\"Zhenxi Song\",\"Zhiguo Zhang\"]","published":"2025-08-07T08:53:08Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false,"code_links":[{"ID":611149,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2885097,"paper_url":"https://arxiv.org/abs/2508.05164","paper_title":"S$^2$M-Former: Spiking Symmetric Mixing Branchformer for Brain Auditory Attention Detection","repo_url":"https://github.com/JackieWang9811/S2M-Former","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}