Title: S$^2$M-Former: Spiking Symmetric Mixing Branchformer for Brain Auditory Attention Detection
Venue: NeurIPS 2025
Accepted: yes
Keywords: Auditory Attention Detection

Spiking Neural Network

EEG

Brain-computer Interfaces
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 ***s***piking ***s***ymmetric ***m***ixing 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.


Peer reviews:
comments: SUMMARY:
Authors proposes $S^2M$-Former, a spiking neural network (SNN) model for auditory attention detection based on EEG signals. This method is the first spiking framework that is capable of mixing features extracted from spatial-temporal EEG dynamics and brain topographical maps. Compare to the common yet naive concatenation of features from both branches, the authors take the path of hierarchical integration. By leveraging spiking neurons, $S^2M$-Former reduces the parameter count and energy consumption.

STRENGTHS AND WEAKNESSES:
### Strengths: 
1. Well motivated and carefully designed components. The functionality of each block is clearly presented in the figure and their individual effectiveness is thoroughly discussed in the ablation study.
2. The comparison and benchmark clearly shows the strength of the proposed model.
3. Compared to prior works like Spikformer (SNN) and DBPNet (ANN), its improvement in efficiency is quite noticeable.
### Weaknesses: 
1. Performance in terms of accuracy improvements is not consistent across different benchmarks.

QUESTIONS:
1. What is the difference between $S^2M$-Former and SM-Former? Is it a simple deletion of spiking neurons? More details about SM-former will be helpful.
2. Which part of the proposed framework contribute most to capture the long-range dependencies?
3. What might be the reason behind the narrowed advantage over prior non-SNN methods (e.g., DBPNet) in the KUL benchmark?

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-24

comments: SUMMARY:
This paper introduces $S^2M-Former$ , a dual-branch, spike-driven architecture that learns complementary spatial (CSP) and frequency-spectral (DE) EEG features through a mirrored stack of spike-friendly modules. The method was tested on three different datasets and achieved state-of-the-art (SOTA) classification results.

STRENGTHS AND WEAKNESSES:
# Strength
+ The paper proposes a novel spike-driven symmetric architecture for auditory attention decoding.
+ While achieving state-of-the-art performance across three benchmark datasets, the method maintains minimal computational cost, reporting only 0.06M parameters.
+ The paper provides comprehensive and extensive experiments, including evaluations under within-trial, cross-trial, and cross-subject settings, as well as detailed ablation studies.
# Weakness
+ Some definitions are unclear or missing. For example, DWConv and PWConv.
+ Although the authors emphasize the importance of reducing computational cost, it's unclear why this method matters.

QUESTIONS:
1) In Section 3.3.2 (SMSC), how is the channel shuffle operation implemented in practice? Is there any difference in the usage during training and testing? It would be interesting to see a performance comparison with and without the channel shuffle.
2) Is the CSP (Common Spatial Pattern) process applied before or after dataset partitioning.
3) As the number of channels and decision window length vary, the parameter count will also change. What specific configuration was used to determine the reported 0.06M parameters?
4) Did the authors use sliding window processing during the training phase? Since all compared methods adopt sliding window processing, was the same strategy consistently applied across all methods for fair comparison?

LIMITATIONS:
YES.
TITLE: Official Review
CLARITY: 2
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-27

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces S2M-Former, a spiking neural network for auditory attention detection (AAD) that combines energy efficiency with effective fusion of complementary EEG features. The model uses a symmetric architecture with spatial and frequency branches, along with lightweight 1D token sequences, achieving competitive accuracy while significantly reducing power consumption and model size.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\n- Introducing spiking neural networks (SNNs) into EEG-based auditory attention detection (AAD) is a novel and promising direction, especially given the growing interest in energy-efficient brain-computer interfaces. This work explores how such models can be adapted to this specific decoding task.\n\n- The proposed symmetric architecture with hierarchical integration of spatial and frequency features seems conceptually reasonable for capturing contextual EEG patterns, aligning with how auditory processing unfolds over time and across brain regions.\n\n- The experimental evaluation is reasonably comprehensive, involving three publicly available AAD datasets (KUL, DTU, AV-GC-AAD) across multiple settings (within-trial, cross-trial, cross-subject), which adds credibility to the reported results.\n\n\n**Weaknesses**\n\n- While the application of SNNs to AAD is interesting, the methodological novelty appears limited. Using spiking networks in EEG decoding is not entirely new, and the main contribution seems to lie in adapting and optimizing existing architectures rather than introducing fundamentally new concepts.\n\n- The hybrid design that combines elements from both SNNs and ANNs raises questions about its practical deployment on neuromorphic hardware. It's unclear whether such a mixed framework can truly benefit from the expected energy efficiency gains in real-world scenarios.\n\n- The performance improvements over existing methods are modest at best. Moreover, while the paper highlights energy efficiency as a key advantage, Table 5 shows only moderate reductions in theoretical energy consumption compared to conventional ANN models — suggesting the gains may not be as substantial as claimed.\n\n- The writing and presentation of the method could be improved. The model description heavily relies on equations with insufficient explanation of key symbols and components, making it difficult to fully grasp the design choices and their implications.\n\nQUESTIONS:\n- The paper emphasizes energy efficiency as a key contribution of the proposed SNN-based model. However, it is not entirely clear why energy consumption is a critical factor in the context of auditory attention detection. Could the authors elaborate on the specific application scenarios where this improvement would be particularly impactful?\n\n- While the results show that the SNN model achieves performance comparable to or slightly better than ANN-based approaches, it’s unclear what exactly drives this effectiveness — is it the spiking architecture itself, or simply the architectural design choices inspired by SNNs? Would an equivalent ANN with similar inductive biases potentially perform even better?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-01"}