S$^2$M-Former: Spiking Symmetric Mixing Branchformer for Brain Auditory Attention Detection
Authors: Jiaqi Wang
Zhengyu Ma
Xiongri Shen
Chenlin Zhou
Leilei Zhao
Han Zhang
Yi Zhong
Siqi Cai
Zhenxi Song
Zhiguo Zhang
Venue: NeurIPS 2025 poster ()
Keywords: Auditory Attention Detection
Spiking Neural Network
EEG
Brain-computer Interfaces
Primary area: neuroscience_and_cognitive_science

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.

M-Forme:: Spiking Symmetric Mixing Branchformer for Brain Auditory Attention Detection
Jiaqi Wang" Zhengyu Ma²; Xiongri Shen', Chenlin Zhou Leilei Zhao', Han Zhang?* Yi Zhong!.s, 1.5, Siqi Cail.6, Zhenxi Song', Zhiguo Zhang -.6
Harbin Institute of Technology. Shenzhen ?Pengcheng Laboratory Peking University
'Harbin Institute of Technology *Great Bay University "Shenzhen Loop Area Institute com; (mazhy)@pcl, sww.hyy@pp..accc.. ac.cn; (zhiguozhang)@hitt edu. cn
Abstract
Auditory attention detection (AAD) aims to decode listeners' focus in complex auditory environments from electroencephalograhhy (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 M-Fomer,,, novel spiking symmetric mixing framework address this limitation through two key innovations: i) Presenting spike-driven symmetric architecture composed of parallel spatial and frequency branches with mirrored modular design, leveraging biologically plausible loken-channe mixers to enhance complementary learning across branches; ii) Introducing lightweight ID token se- quences to replace conventional 3D) operations, reducing parameters by 14.7x. The brain-inspired spiking architecture further reduces power consumption, achieving a 5.8x energy reduction compared to recent ANN methods, while also surpassing existing SNN baselines terms parameter efficiency and performance. Compre- hensive experiments on three AAD benchmarks (KUL. DTU and AV-GC-AAD) across three settings (within-trial, cross-trial and cross-subject) demonstrate that M-M-Former achieves comparable state-of- of-the-art (SOTA) decoding accuracy, mak- ing it a promising lower,wer, high performaace solution for AAD tasks. Code is available pettps::/giitee http:///......ccccaacana//////// Former
Introduction
The "cocktail party effect" refers to the remarkable ability of the human auditory system to isolate and focus on specific speaker's speech in competitive background and noise environment 212) This capacity, realized through dynamic neural processing in the auditory cortex and top-down attentional modulation 3. has profound implications for understanding human auditory cognition and developing neuroengineering applications. Auditory attention detection (AAD) investigates the brain's 's selective hearing ability by detecting which sound stream a listener is focusing on, based neural recordings, as illustrated in Figure. [ It aims to address critical challenge in neural rehahilitation: How restore natural auditory scene analysis for individuals with hearing impairments.
in non-invasive electroencephalograahy (EEG)-based approaches have demonstrated remarkable success in reconstructing attentional selection patterns from cortical re- sponses. On the one hand, AAD enables hearing aids dynamically amplify the speech stream that the user is focusing on, providing more natural and adaptive listening. On the other hand, integrating
Recent advances 5
"Coresponding author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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AAD into brain- comppce interface (BCI) systems allows real-time feedback between the brain and auditory devices, mimicking thalamocortical feedback pathways observed in natural auditory processing 8 As AAD is increasingly integrated into wearable systems such as hearing aids 9 IO] and low-power BCIs I1I, the need for models that are both accurate and energy-efficient becomes critical, due to strict constraints on battery life, latency, and computational resources. These practical demands highlight the importance lightweight AAD models for real-world deployment.
Feature extraction techniques such as common spatial pattern (CSP) [2 [3 and differential entropy (DE) [5 have demonstrated effectiveness in capturing discriminative characteristics from auditory eevokor--evoked EEG signals. Despite recent progress, AAD remains hindered by the absence of synergistic frameworks that can fully leverage complementary EEG features under energy- energy-efficiency constraints. This limitation particularly critical for real-world AAD applications, which require lightweight, low-power solutions for high-performance, closed-loop systems in hearing aids. Although recent tual-branch networks [ that incorporate multiple EEG features have shown improved performance over single-brane bragle models, they still face several challenges. First, most of these methods adopt an isolated learning paradigm, combining features via simple concatenation or summation, neglecting the potential hypothesis that the complementary learning among EEG features can benefit performance [6. 17 [8]. Second, their designs introduce substantial computational overhead, especially when modeling specific EEG properties (e.g., topological structures) using resource-intensive operations such as 3D convolutions, thereby limiting deployability.
Speaker1
Evoked EEG Signal
Right
Auditory-driven Attention
Left
Attended Speaker
Speoker V
AAD Model DDa
Speaker2
Speoker2
X
Figure 1: EEG-based AAD Paradigm.
To tackle the above challenges, we introduce M-F-Frrrrr, spike-driven symmetric model that is naturally endowed with low power consumption by replacing energy-intensive multiply-accumulan (MAC) operations with sparse, spike-based accumulate (AC) communication, while retaining the precise temporal dynamics essential for modeling auditory attention. Specifically, the proposed M'M-Former brings several key advantages. Firstly, the symmetric design enables parallel extraction of spatial and frequency- -omann representations using mirrored modular structures. This design encourages complementary learning across branches by facilitating the synergistic fusion of domain- specific representations, thereby replacing isolated learning. Within each branch, multi-level modules capture hierarchical contextual dependencies, enabling expressive and informative representations without the need for complex customizations. Secondly, to address the high parameter count and computational complexity associated with dual-branch bringing. M-Fomer embraces a lightweight, efficient architecture. By replacing power-hungry 3D operations with streamlined ID token repre- sentations, we drastically cut down on computational overhead, achieving superior computational efficiency while preserving high performance. The above makes M-F-rrr particularly suited for goegy-effiicient neuromorphic neuro-steered devices.
We validate our model on three public AAD datasets: two uni-stimuli (audio-only) benchmarks, KUL [19 and DTU [20), and one multi-stimuli (audio-visual) benchmark, AV-GC-AAD (21), under within-trial, cross-trial and cross-subject settings. Compared to recent dual-branch models [6] 7), S- reduces the parameter count by 14.7 times and power consumption by 5.8 times, all while achieving competitive SOTA performance. In comparison to the latest single-branch SOTA models 5 15). S M-rmmrr only maintains fewer parameters but also roundly surpasses them. Furthermore, it achieves superior performance against recent influential SNN backbones. These results collectively underscore the effectiveness and efficiency of our proposed 3'M-Former in tackling the challenges posed by AAD tasks. Our contributions can be summarized as follows:
1) We propose M-Frmer, the first spiking symmetric mixing framework for auditory attention de- coding tasks, which enables effective hierarchical integration of contextual representations within each branch. By leveraging biologically plausible mixers, our design naturally promotes complementary learning across EEG feature branches, thereby notably improving performance.
2) S M-Former reduces the parameter count by up to 14.7x and energy consumption by 5.8x compared to recent dual-branch models, without requiring any complex customization. Its spike- driven architecture fully delivers higher decoding accuracy than both its ANN counterpart and existing SNN baselines, highlighting a compelling trade-off between efficiency and performance.
2

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3) We validate M-rmerr across three AAD datasets and multiple evaluation settings, where it achieves competitive state-of-the-e art performance. These results demonstrate its strong generalization, offering new perspective in low-power brain-computer interfaces for AAD tasks.
2 Related Works
Brain Auditory Attention Detection. early development, CSP-CNN [2] combined CSP with CNNs to enhance the non-linear modeling capacity, demonstrating the potential for robust AAD. Following this, SSF-CNN [4] focused on the topographic distribution alpha-band EEG power and introduced spectro-spatee DE extraction strategy to decode auditory attention. MBSSFCC [TS] extended the framework by incorporating multi-bane band frequency analysis, extracting DE features, and applying a ConvLSTM module for spectro-spatial-temee -tmpiiall learning. Recently, DBPNet 6 proposes a dual-branch network for AAD, which consists of a temporal attentive branch and a frequency 3D convolutional residual branch, and fuses the lemporal-frequency domain features by concatenating the outputs from branches into a fusion vector. M-DBPNet 7 is an upgraded version of DBPNet. This framework introduces Mamba-based [22] methods in the temporal branch, aiming better extract temporal features from sequential embeddings, with a few additional parameters. DARNet is a dual attention refinement network designed to capture spatio-temporal representations and long-range dependencies in the CSP feature patterns, achieving near dual-branch performance.
Spiking Neural Networks. Recognized the third generation of neural networks [23] spiking neural networks (SNNs) effectively mimic the dynamics of biological neurons with sparse and asynchronous spikes 24 This approach enables SNNs to achieve high computational performance
with low energy consumption, making them a viable energy-efficien -fficiiet alternative to artificial neural networks (ANNs) [25] Despite these advancements, recent SNN methods for AAD [26] 27 28 still showcase weaker performance compared to recent SOTA ANN models, primarily due to their sparse feature representation and relatively simple network architectures, and face challenges such as closed-source implementations and limited reproducibility, making direct comparisons difficult.
3 M-Forrr
As illustrated in Figure. 2 S is an SNN model that leverages membrane potential- based transmission 229 with shortcut connections to maintain spike-driven dynamics across layers. Given an EEG series F, we first extract spatial-tempee features (Fs RCXT with C' denoting channels and T time points) using CSP and rrequeny--spectral features (Ep E 5 H where H x W is the map size) via DE, as detailed Appendix [A.I] These embeddings are then expanded along the temporal dimension over T's steps and encoded by branch-specific spiking encoders. The representations are further refined through a series of spike-driven modules within the spiking symmetric mixing M) block, effectively capturing complementary spatial-frequency patterns. Finally, the fused D-dimensional embeddings are passed through classification head to produce the prediction Y.
3.1 Spiking Neuron
Spiking neurons serve as bio- bio-plausible abstractions of neural activity [30 31). We adopt the Leaky Integrate-and and-FFre (LIF)
32 neuron for intra module communication, whose discrete-time dynamics are defined in Eq. 20 Eq. 21 and Eq. (2)). To enhance membrane potential awareness across inter-module connections within the M-rrrrr, we propose a novel neuron variant: the channel- wise parametric LIF (CPLIF) neuron. It builds upon the parametric LIF neuron variant of LIF with learnable membrane time constant that enables adaptive temporal control [33 by assigning an individual time constant to each channel. This design enables channel-wise adaptive modeling of temporal dynamics, allowing more expressive and finer-gras finerngrained spiking activation across time steps. We denote the CPLIF as SNhead
head in subsequent sections. Its membrane potential update is given by:
1 H c, = V(L11, π (X(t, (VV[cc,))(VV[-1.....
1,
Vreset)) + A[c],
where TI, 3 RC are learnable vectors for the channel-wise membrane time constants and bias, respectively. c indexes the channel dimension and n denotes the token index within each channel. The CPLIF shares the same firing and reset equations the LIF, with details provided in Appendix A.2
3

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(a) SoM-Frrrer
Spiking Symmetric
EES Signal
Sonts Branch
Ereeder
Sprking Channel wis: Selt Attercioon
Membrrr Pementtes Pa
Muti-ecale Separable Comollution
Memberne Prttentt
Deroding Head
Freguenccchond
Spliing
Spiking
TopograpiieMpppp Map
(reaurney trano
DE
Es
Excddtr
Spiking
Spiking
2D Plane
Branci-specific Spiiking Encoders (c) Spiking Channel-wise Self Attention (SCSA)
(d) Spiking Multi-scale Separable Convolution (SMSC)
Spottal Branch
Soen Couzi
Encoder
W
Lurnet 2
W.
Sf Ci
W
Trarpooe T
x
Temporal
Seate
bermat
bartei 3
Jurter -
P HD
3N
Shareetsvulle
(e) Spiking Gated Channel Mixer (SGCM)
Membrane Potential-aware Token Mixer (MPTM)
Frequency
DWCCorL
Spatisl
Query
Conda
Aoyrogter
Rolne
Dilaeed BlatedCCond CarnaD
Fusion
DWKonviD
Rofrie
Frecuency
Frequency
LE Neuron CRUI No Postion Innnding
Manni wwtpcattiinnnnn
Bleeer Add
Conceieracion
Unadamard product
Chantai--aa Spltt
Figure 2: M-rrrrr A dual-branch mirrored architecture comprising branch-specific spiking encoders for diverse domain features, leveraging spike-driven symmetric modules for contextual representation learning and enabling effective complementary interactions across parallel branches.
3.2 Branch-specific Spiking Encoders
Spatial Branch Encoder (SBE). To effectively capture spatio-temporal EEG dynamics design a spike-driven SBE that jointly models temporal dependencies and spatial correlations across electrodes. The SBE comprises
key components: (1) biologically plausible spiking neuron dynamics to enable spike-driven representation learning; and (2) multi-stage mechanism that integrates progressive condensation with refined integration. Given the input feature map Es RdxxCXT we first apply a cascade of temporal convolution layers (TConv2d) with increasing kernel sizes (e.g., k 8) to progressively extract temporal dependencies. Each temporal layer is followed by batch normalization (BN) and a spiking neuron S.N(.). The operations are formally described as:
F's (B (F)))))))))))))))))))))))))))))) E PXx22XxXXT
Eg
(Ex)) (E'))) E TTxADxxXTT
Next, we introduce a dual-path spatial convolution module (SConv2d). each path employing full-channel spatial filter with kernel size (C 1), which aggregates cross-channel interactions. The outputs of both spatial paths are then refined via residual addition to enhance the spatial representation:
E SN(BB(SCCno2ddoooom (E"))) + (N(EEN(CCom/
E TsxXXXT (4)
are primarily EEG 2D brain topography, propose novel FBE, which addresses both the representations from information and eegion-specific activation patterns inherent in munti-band EEG topographic data. FBE begins by applying three successive spiking convolutional operations:
Frequency Branch Encoder (FBE). Recent spiking patch splitting modules 33 35
designed for visual inputs. To effectively extract recquenc--ppectraa-ee
Epp (A((BN(Con)22((E))) E Ts XxW LE (1,
(5)

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where Ep, E R PxW is the input at the I-th block (L = 3), and each 3 convolution employs dilation rate of expand the receptive field. This enables the model to capture spatially dispersed yet salient edge-region activations more effectively [36]. In addition, the channel dimensions are progressively transformed via D - 4D - 2D D, followed by max-pooling layer for spatial compression. To further refine the representation, we apply 1 x spiking convolutional layer with a residual connection, enhancing representation stability and robustness.
3.3
S M Module
After processing through the spatial (SBE) and frequency (FBE) encoders, EEG embeddings are unified ID tokens X RTxxNXDD where Ts denotes the number of time steps, N is the number of tokens, and D is the feature dimension. As shown in Figure. (a), the S block hierarchically refines these representations through series of mirrored spike-driven modules: the SCSA module first captures long-range intra-branch dependencies, followed by SGCM and MPTM for cross-branch fusion across channel and token dimensions. SMSC then extracts local fine-grained details, and second combination of SGCM and MPTM further reinforces symmetric complementary integration.
3.3.1 Spiking Channel-wise Self Attention (SCSA)
Recent spiking attention mechanisms have made considerable progress, with spiking self-attention (SSA) being one of the most widely applied methods [34]. In our implementation, we modify the SSA mechanism by adopting channel-wiss protocol to enhance interpretability, where the spatial branch reveals electrode correlations, and the frequency branch uncovers multi- band relationships. Moreover, this adjustment reduces the spiking attention score matrix from N x N to D D (37, leading a lower computational complexity from O D) to O(ND2) for greater efficiency. As illustrated in Figure. (c), we define this module SCSA. Specifically, given input EEG embedding X E R T's xD the computation proceeds as follows:
Xs (W,,WWWWW))) (SW head (X))) E TsXNXD
(6)
Qs = ( (X())xs),,))))))))))()())) Kg = SN (W (Xs)), Vs SNv
(7)
U SN (K33Vs) * a) E T2XNXD
(8)
Xout = SNo E TSXNXX.
where Qs, Ks, and Vs are the spiking query, key, and value vectors, each of size R NxD 0 denotes a scale factor. Each learnable projection matrix W is implemented as a 3 kernel size of 1D depth-wise convolution, allowing spatially local parameter-efficien feature transformation.
3.3.2 Spiking Multi-scale Separable Convolution (SMSC)
We propose the SMSC module, which leverages multiple depth-wise convolutions with different receptive fields enrich multi-scale patterns in EEG embeddings. As shown in Figure. 2 d), the module comprises three parallel depthwise convolutional paths with different receptive fields, followed by channel shuffle operation to promote cross-scale and cross-channel information integration. Given the input spiking feature X E IR T'sx NDD we first apply CPLIF neuron SN head- followed by pointwise convolution (PW Convld) and batch normalization:
X' (N (RNCPon Conv1 (SNN (ennnld(Sneea(( (X)) E R x
(10)
Here, PWConv expands the channel dimension for later multi-scale processing. The feature X' is evenly split into three parts along the channel dimension. Each part is then processed by a depthwise convolution (DW Convl ds;). where distinct kernel size k; E {1,3,5) is assigned to extract localized patterns at different temporal scales:
X BN, (DW Conelds, (Split(X',
E RTxXNDD; iE (1,33
(1I)
Unlike standard convolutions that aggregate information across channels, depthwise convolution processes each channel independently. thereby reducing computation but limiting inter-channel interaction. To compensate for this, we adopt a parameter-free channel shuffle operation (38] after summing the three branches:
X = Shuffle(X"" + X²" X
(12)
This operation rearranges the channel dimensions by grouping and permuting features to facilitate cross-group feature integration, thereby enhancing representation diversity without adding complexity.
5

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3.3.3 Spiking Gated Channel Mixer (SGCM)
The SGCM module is designed to adaptively fuse multi-channel spatial and frequency represen- tations. Firstly, Xs and Xp are concatenated which can form as X Concat(Xs. xy) E (Tsx(NN+N))xD where Ng and Nf are the number of brach-specific tokens, respectively, and D is the feature dimension. The concatenated features X' are fed into a spiking neuron followed by linear transformation, whose channel dimensions are projected from D 2D, then passed through a convolution stem to refine the representations:
X" (Coooolld (SiieSSSSLiiiieaA head (X))))) R Nox
(13)
As presented in Figure. (e), the core of the SGCM integrates spiking gating mechanism, modulating the flow of information across channels. Specifically, the X" is split into two components,
Xo, Xx = SN q2 (BN,,6 (DW Conug,, (SN q. As (Split(X" 2))))) E TRTsxNNN+N) (14)
where XQ and Xx sever as the query and key value 39 respectively, and the calculation process of the gating mechanism can be formulated as: A = SW i= XQ.,) Ts x (N where A,
the channel attention vector, which models the importance different channels. Then the channel- wise mask is adopted by X = A C Xg. Finally, the attention-modulat gated embeddings are calculated generate the final output:
X fusion = Liinea(SN) out (BN (CN(olll((X"")) IR (
(15)
3.3.4 Membrane Potential-aware Token Mixer (MPTM)
As illustrated in Figure. (O) the MPTM is primarily structured as two parallel operations: the fusion branch from SGCM modulates the frequency and spatial branches, respectively, For clarity, we detail the interaction between the fusion branch X fusion and a generic branch XG E x which denotes either the spatial or frequency branch, and decompose the MPTM into three functional stages.
Core Representation. We implement global average pooling (GAP) as an Aggregator over the token dimension to encode global information across all tokens and thereby capture long-range dependencies [40 This operation condenses the spiking membrane potentials into compact global summary that serves as the core representation for cross branchh fusion, formulated as:
X/ = (AP) E T Xjusion GAP(SN head headd X {fusion) E R (16)
Representation Refinement and Fusion. To facilitate fine fineggrained potential-aware modulation using the core representation, we construct a refined guidance representation, denoted as R E xT>xNx by proportionally combining the global summaries from two branches. Specifically, we divide the total number of tokens N into two parts: NG = [a:N] represents the number of tokens filled from the generic branch, and N Jusson N NG tokens are filled by repeating from the fusion branch, where we set a = 0.5. We then repeat and concatenate them, which can be written as:
R = Concat X No), Repeat(
X Fussion fussion) E TsxNxD
(17)
The final fused output is denoted as F, which integrates the refined guidance and the original primary features through spiking clement-wiss modulation with residual connection, formulated as:
F SNo()) @ R+ X E RTsXNND
(18)
Output Generation. The final output is generated by concatenating the fused representation with the compressed original input XG. where max pooling is applied with a kernel size of 3 and a stride 2.
Xout = Concat(F, F, E R Tsx N
NNXD
(19)
4 Experiments
Datasets and Processing. We evaluate the performance of M-M-Former across three publicly available AAD datasets: KUL and DTU, which focus on auditory-only stimuli, and AV-GC-AAD, which includes audio-visual stimuli. summary of the datasets is provided Table D Each dataset is preprocessed using pipelines aligned with prior AAD studies to ensure consistency across

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Table 1: Comprehensive statistics and details for the three AAD datasets.
Duration per trial (seconds)
Duration per subject (minutes)
Total duration (hours)
Dataset
Subjects
Scene
Language
Trials
KUL DTC
19
16
audio-only
Dutch
8
360
48
12.8
audio-only
Danish
60
15.0
AV-GC
audio-visual
Dutch
14.7
methods: The KUL dataset [19] is re-referenced to the central electrode, bandpass filtered (0.1-50 Hz), downsampled to 128 Hz, and trimmed to the same trial length. The DTU dataset [20] is high-pass filtered at 0.1 Hz, downsampled joint decorrelation, followed by re-referencing to the channel average. The AV-GC dataset [21] is bandpass filtered (1-40 Hz), re-referenced to the average of all channels, and downsampled to 128 Hz [4]] To avoid contaminating preprocessing signatures that could affect CSP filters [21]. no z-scoring time normalization is applied any of the datasets. More implementation details are provided in Appendix A.3 and A.4
128 Hz, and denoised at 50 Hz. Eye artifacts corrected via
Evaluation Methods. The subjects in the three public AAD datasets were instructed to focus of two simultaneous speakers, with the directions being left and right, formulating it as classification task with the binary labels. We evaluate model performance using the average accuracy and standard deviation (SD) across all subjects for each experiment and decision window. We reproduce and
compare five publicly available baseline models (details in Section 2): SSF-CNN [14], MBSSFCC [13], DARNet [5], DBPNet [6]. and M-DBPNet under two subject-dependen evaluations and a subject-independent evaluation:
Within-trial 5
15: EEG data from a single trial are split into 8:1:1 train, validation and test sets. The final train/validationttes sets are obtained by concatenating data across all trials.
To prevent overfitting to specific EEG segments, all trials are randomly split into 8:1:1 for training, validation, and testing. For trials with fewer than 10 samples, two with different labels are randomly chosen for testing, and the rest are proportionally divided.
Cross-trial 7 42 43
Cross-subject [33: We follow cave--oe-subject-ou (LOSO), where leaving out one subject's data as the test set while training the remaining subjects, iteratively performing cross-validation.
To address real-time response needs and ensure fair benchmarking, all models are evaluated under short decision windows of 0.1s, 1s, and 2s. All methods (including baselines) adopt the same preprocessing pipeline, followed by EEG segmentation using a sliding window with 50% overlap, in line with standard practice in prior AAD works. Feature extraction is performed separately per set to prevent information leakage.
Table 2: Within-trial results under three datasets. Color shading:
Highest, Second, Third.
Accuracy (%) SD
Datascts
Models
Architecture
Params (M)
2-second
1-second
0.1-second
SSF-CNN 14
4.21
79.64 + 9.64
76.63 # 10.28
77.73 + 9.60
MBSSFCC DARNet
Single
16.81
93.71 6.46
92.65 7.48
84.02 # 9.39
KUT.
0.08
92.81 9.45
92.04 9.75
87.66 10.79
DBPNet 6
0.88
93.66 7.88
93.25 + 7.33
85.70 + 9.75
M-DBPNct
Dual
1.32 1.00 0.88
93.75 6.34
93.19 7.28
86.16 + 9.94
M-Fororrer (ours)
0.06
93.71 # 8.14
8.66 92.27
83.39 12,80
SSF-CNN 14
4.21
70.65 + 6.18
67.63
4.35
65.44 = 4.72
MBSSFCC
Single
16.81
80,20 + 7.62
76.64 1 7.97
69.43 + 5.59
DARNet
0.08
81.30 = 5.76
79.89 7.88
76.04 = 6.60
DTU
DBPNet n
0.88
83.93 + 5.17
80.69 6.54
77.06 + 5.08
M-DBPNct
Dual
1.32 1.00 0.88
82.56 8.01
81.12 # 6.82
74.06 + 5.68
SrM-Former (ours)
0.06
85.28 6.01
82.87 6.92
75.84 + 5.46
SSF-CNN 14
4.21
79,50 + 8.45
76.40 + 7.96
66.67 + 5.37
MBSSFCC
Single
16.81
89.13 + 7.21
87,90 1.900 6.98
72.17 + 5.77
DARNet
0.08
89.17 6.94
6.87 88.31
79.45 :: 7.70
AV-GC
DBPNct 6 M-DBPNct
0.88
90.78 4,91
89.04 5.155
73.37 + 6.13
Dual
1.32 1.00 0.88
87.04
7.76
86.26 7.35
72.88 + 6.73
M-Forrer (ours)
0.06
91.83 6.66
89.24 7.59
74.42 & 7.79

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Table 3: Cross-tria results under three datasets with color shading:
Highest, Second, Third.
Accuracy (%) + SD
Datasets
Models
Architecture
Params (M)
2-sccond
1-second
0.1-second
SSE-CNN T4 MBSSFCC DARNel S
4.21
60.32 19.25
58.59 = 17.27
61.93 15.88
Single
16.81
73.56 23.98
71.01 22.50
65.12 20.63
0.08
68.92 24.06
68.43 * 24.18
68.01 22.63
KUL
DBPNet
0
0.88
72.95 + 24.36
70.89 + 25.01
65.67 21.84
M-DBPNet
Dual
1.32 1.00 0.88
74.27 + 21.37
70.64 + 23.82
66.97 21.87
S*M-Former (ours)
0.06
72.39 25.21
71.22 25.97
66.49 21.03
SSF-CNN 44
4.21
69.50 7.28
67.25 7.44
65.21 9.63
MBSSFCC L
Single
16.81
76.53 a: 8.84
75.55 +: 8.86
70.08 8.36
DARNet 5
0.08
72.41 + 9.73
71.98 = 10.03
69.11 8.67
DTU
DBPNet i
0.88
76.40 :: 9.53
74.25 + 10.03
66.54 6.92
M-DBPNet
Dual
1.32 / 1,00 / 0.88
76.18 + 9.18
74.86 = 9.50
67.57 8.12
M-r-Frrmer (ours)
0.06
76.74 4: 9.96
75.75 + 9.96
70.36 7.31
SSF-CNN 14
4.21
64.42 11.14
63.45 + 9.87
59.62 7.79
MBSSFCC 15
Single
16.81
63.67 16.48
60.82 # 13.82
60.19 19 10.04
AV-GC
DARNet
5
0.08
64.99 = 13.67
64.15 = 13.50
63:47 12.34
DBPNet
6
0,88
64.77 17.00
64.37 = 16.28
60.92 8.82
M-DBPNet
Dual
1.32 / 1.00 0.88
68.90 17.72
64.88 = 15.33
61.03 = 9.53
S*M- MM-Former (ours)
0.06
70.64 18.65
65.77 = 15.58
65.49 13.74
Results
We present a comprehensive evaluation of M-F-rer with five AAD methods across three datasets (KUL, DTU, AV-GC) in terms of architecture, size, and decoding accu- racy under within-trial and cross-trial settings, as shown in Tableeaand Table Our M- achieves the highest decoding accuracy in 11 out of 18 conditions, surpassing dual-branch counterparts DBPNet (6) (2/18, 11.1%). M-
Bange BN 03 GR
Moder
Mean
Cudle
Accuracy (%))
DBPNet 7 (2/18, 11.1%) and the single-branch SOTA DARNet 5 (3/18, 16.7%). while securing 83.33% Top- 3 coverage (15/18), exceeding DBPNet (12/18, 72.2%) and M-DBPNet (11/18, 61.1%). and DARNet (9/18, 50%). Overall, the performance of dual-branch models tends to outperform branch models. Especially, S M-Forrr excels in cross-trial performance, achieving SOTA results on the DTU dataset (e.g.. 76.74% = 9.96 for 2s) and AV-GC dataset (e.g., 70.64% + 18.65 for 2s). demonstrating its robust generalization ability on unseen EEG data compared to recent methods. Notably, these results are achieved with only 0.06M parameters, using fixed model size across all window lengths, unlike M-DBPNet, whose size varies by window (e-g.. 1.32M for 2s. 1.00M for Is and 0.88M for 0s).). In comparison, M-M-Former is 14.7x smaller than DBPNet (0.88M) and 22x smaller than M-DBPNet (1.32M), yet consistently outperforms them. DBPNet and DARNet also have fixed sizes but with substantially more parameters than S Fororrrer. Furthermore, decoding accu- racy improves with larger decision windows, as extended context allows for more detailed attention estimation, con- sistent with prior research 14
5'M-Fomer
MoDePhet
(a) KUL Dataset
Rarge
1.0
0.7-
Accuracy
0.5
Charcs 16.1
S/M Formor DEPNict
0BPNic
AV-GC Dataset
Figure 3: Comparison across all sub- jects two datasets under cross-trial.
Although our model exhibits robust performance across all benchmarks under the cross-tria setting, as presented in Table 3, notable performance gap remains when compared to within-trial scenario. This disparity is especially pronounced on the KUL. and AV-GC datasets, where elevated standard deviations reflect the increased difficulty of cross-trial generalization. Such challenges are largely attributed to greater intra-subject variability in EEG signals across trials and the inherent

---

Table 4: Ablation study and compare with recent SNN models on DTU dataset.
Within-trial
Cross-trial
Methods
Feature Embeddings
Puram (M)
Time Steps
2s
1s
2s
1s
S-M-Former (proposed)
CSP+DE
0.06
85,28 = 6.01
82.87 + 6.92
76.74 +: 9.96
75.75 : 9.96
SM-Former (ANN)
CSP+DE
0.06
80.94 = 5.66
80.30 + 6.77
73.49 + 8.09
73.48 4 7.98
CPLIF + LIF
CSP+DE
0.06
84.13 = 5.96
81,66 +
75.67 9.77
74.67 9.88
SGCM MPTM
CSP+DE
82.98 * 6.25
80,96 +
74.81 +9.3
74.12
Spatial Branch
CSP
0.04
82.28 + 7.13
80.05 1: 6.69
73.58 10.21
73.19 9.71
Frequency Branch
DE
0.01
70.48 = 7.78
69.07 + 6.78
70.11
68.98 + 8.00
QKFormer 39
DE
0.29
69.54 = 8.80
68.84 7,46
69.67 9.84
67,83 8.85
Spike-driven Tfrmeformer
DE
0.37
69.44 = 8.11
7.30
68.29 8.71
68.06 8,77
Spikformer
0.37
67.28 7.93
67.15 6.52
69.27 8.41
65.31 9.00
To assess model robustness, we conducted visualization analysis covering three decision windows and all subjects. While typically focused on a single decision window for their models, we extend the evaluation across multiple models and temporal ranges. As illustrated in Figure. 3 box plots show that subject-level performance more dispersed in the cross-trial scenario, with several subjects falling below chance level (CL (0%) 455 across all methods. Based on interquartile range (IQR) and the count of below-CL outliers, M-FFormmr exhibits more consistent behavior across datasets and decision windows, suggesting favorable robustness under zero-shot settings. More detailed statistics and analysis are provided in Appendix A.6
challenge of generalizing to unseen data (zero-shot conditions 44
prior studies 7 42
We further conducted an ablation study to analyze our proposed model from two perspectives, as shown in Table 4 First, we replaced the spike-driven components in M-rmer with pure ANN structure, termed SM-Former (see Appendix AA.5 detailed conversion steps). This modification led to a notable drop in decoding accuracy across all settings and time windows. Specifically, in the within-trial setting with the 2-second time window, accuracy dropped by 4.34%, from 85.28% (SD: 6.01) to 80.94% (SD: 5.96), highlighting the effectiveness of our SNN-friendly components in learning representations through spike-driven dynamics. From another perspective, we first validated the effect of the CPLIF neurons by replacing them with standard LIF neurons. This further degraded performance, with accuracy dropping from 76.25% (SD: 9.79) to 75.30% (SD: 9.77) for 2-second decision windows, and 75.75% (SD: 9.96) to 74.67% (SD: 9.82) for 1-second under the cross-trial setting. We then removed SGCM and MPTM entirely, as SGCM's output serves the input for MPTM, and retained only the concatenation for fusion. The removal of this combination led to further degradation in decoding accuracy and an increase in standard deviation. This empirical outcome substantiates our initial hypothesis that the integration of complementary learning modules can enhance model performance. Finally, we compared the performance of the individual branches (Spatial- and Frequency-Brann worse, with the spatial branch performing better than the frequency branch. Notably, under the same time step T's = 4. the frequency branch surpasses three transformer- 39] (0.29M), Spike-driven Transformer (SDT) (35] (0.37M), and Spikformer (34) (0.37M). See Appendix A.6 for more results. The above impression can be attributed to two main factors: (1) the spike-driven hierarchical architecture within each branch helps capture both long-range dependencies and fine-grained details efficiently; (2) the symmetric structure encourages complementary learning between the spatial and frequency domains, enabled by biologically plausible token-chamin mixers.
with the M-M-Former. The individual branches performed both
SNN models, QKFormer
Table 5 analyzes the efficiency advantages with detailed theo- retical energy consumption pro- vided in Appendix A.. Under 2-second windows, S'M-Former achieves exceptional efficiency in dual-branch setups: 93% smaller size compared to DBPNet (0.06M vs. 0.88M, 14.7x reduction), 82.8% lower energy than DBPNet (0.0779 mJ vs. 0.4526 mJ, 5.8x re-
Table 5: Estimation of sizes and operational counts across single- branch and dual-branch models under cross-trial setting,
Model
SNN
Params (M) J
FLOPPs (G)
SOPs (G)
Energy (mJ) J
DARNet
X
0.08
0.0054
0.0247
OKFormer
0.29
0.0015
0.0160
0.0212
SDT
0.37
0.0015
0.0227
0.0272
Spikformer
V
0.37
0.0015
0.0065
0.0126
DBPNet
X
0.88
0.0984
0.4526
M-DBPNet
X
1.32
0.1068
0.4913
SM-Former
0.06
0.0243
0.1116
S'M-Frrmer
0.06
0.0112
0.0293
0.0779
duction), benefiting from its efficient lightweight architecture and spike-driven mechanism. Compared to its ANN counterpart, 53.9% fewer FLOPs than SM-Former (0.0112 G vs. 0.0243 G). Although

---

S-M--rrerr's 's energy consumption (0.0779 mJ) is higher than that of single- branch SNNs, this is due the additional FLOPs from the dual-branch input embeddings. When considering synaptic operations (SOPs), our model generates comparable SOPs to other single-branch SNNs (0.0293 G vs. 0.0227 G, 0.0160 G, and 0.0065 G), indicating the effectiveness of our module design. Although DARNet shows energy efficiency, it still substantially lags behind S M-F-orr in performance level.
We further conduct cross-subject (LOSO) validation on the KUL and DTU datasets to evaluate the model's 's robustness across subjects, consistent with previous methods 4 This setting presents a more challenging and realistic evaluation scenario, the model must gener- alize entirely unseen subjects. As summarized in Table 6 proposed Mrr-rorrer consistently outperforms all competing methods across both I-second and 2-second decision windows. Notably, achieves the highest accuracy of 75.75% on KUL and 59.75% on DTU under the 2-second window, while maintaining competitive performance even in the more context-constrained 1 1-second window. These results underscore the strong generalization ability of S M-Former across broad experimental settings, validating its effectiveness in real-world AAD scenarios where inter-subject differences are prominent.
Table 6: Leave-one-subject-ouu cross-validation experiments.
KUL
DTU
Model
2-second
1-second
2-second
1-second
DARNet DBPNet
74.65 15.77
73.72 15.09
59.12
58.48 = 4.67
74.82 13.40
71.77 # 14.07
56.83 5.00
54.76 4,38
M-DBPNet
72.83 12.49
71.72 13.35
5.54
53.56 6.16
M'M-Foreer
75.75 13.43
74.37 # 12.57
39.75
57.70 4.21
Conclusion
In this work, we introduce S M-Foorr,,, an efficient spiking symmetric mixing network designed to address key challenges in AAD tasks. By integrating spike-driven hierarchical modeling with opatial-frequency complementary learning, our model achieves high accuracy while maintaining computational efficiency. MrM-Former not only delivers competitive statc-of-the-ar performance, but also demonstrates robust generalization across diverse evaluation settings, including unseen data and subjects. Furthermore, it outperforms recent lower energy consumption, while avoiding isolated learning paradigms. These advantages make S² M-M-Frrmer promising neuromorphic solution for energy mergy-efficient auditory attention decoding.
models with fewer parameters and
Ethics Statement
In this work, we do not generate new EEG data, nor we perform experiments on human subjects. We the three publicly available KUL, DTU, and AV-GC-AAD datasets without any restrictions. The public download links are as follows:
KUL 19
46
#tttss//zenoo.oor//rerr///////0
DTU
20
47
sttpa://zandooorr//reeer///////
AV-GC-AAD 21
48
https://zanodd.org/eeeoodd/d///
Acknowledgments
This work is supported by the National Science and Technology Innovation 2030 Major Project (No. (0255D0215501) the National Natural Science Foundation of China (No. 82272114) and the Shenzhen Science and Technology Program (No. ZDSYS2023062009 1203008).
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A Technical Appendices
A.1 EEG Feature Embedding
We segment the evoked EEG into decision windows employing the sliding window method. Each decision window E is represented as an T matrix, which formed as E [e1. e,,..., ee] E RCXT, where T denotes the length of the window, and C' represents the number of EEG channels. Each column e; corresponds to the EEG signal across all channels at a specific time sample t.
As shown in Figure, 2 our proposed M-Forrr adopts a dual-branch architecture to process two types of feature embeddings derived from evoked EEG data E. For clarity, we refer them as the spatial branch and the frequency branch, and elaborate on the corresponding input embeddings Eg and Ep in the following sections.
For the spatial branch, the CSP algorithm is adopted 12]. which has demonstrated the effectiveness of extracting primary spatial features for AAD tasks. Specifically, CSP finds the optimal spatial filters using covariance matrices [3]. The processing can be formed as Eg = CSP E)) E Rd xT where d, is the number of components to decompose EEG signals. Similar the DARNet [5]. we select the dn = C = 64. It is worth noting that some previous works 6 7). have regarded the CSP-extracted branch the temporal branch. Although Es retains the sampling points T, we prefer define it as the spatial-(temporal) branch.
For the frequency branch, firstly decompose the EEG signals into five frequency bands (from band to y band), considering the power spectrum of different frequencies, we further extract the differential entropy (DE) feature from each frequency band and then project them onto size of H x W 2D topological maps to utilize their topological patterns [4, 15]. Specifically, in the implementation, H W 32. Taking specific frequency band as example, the spectral feature can be formed as EP.- = (E(E.) E R xxx where denotes the ith frequency band. Finally, we concatenate five maps into a Er [EF1, EF,,..., ..., Ers] R
A.2 Spiking Neuron
To achieve intra-module communication, we employ the Leaky Integrate-and-Fiin (LIF) as the spiking neuron. The following discrete-time equations govern the dynamics of LIF neuron:
H = Vlt - 1] ((Vit 1] - Vreeet)) + X(t).
(20)
S| θ ( Ven) VtJ [[] (1 S(1) + VieserS(t).
(21)
(22)
where 7 is the membrane time constant, XX [t] is the input current time step Vreser is the reset
potential, and VrA is the firing threshold. Eq. (20) describes the membrane potential update, while Eq,
(21) models spike generation, where (vv) the Heaviside step function: if H[! > Vth, O(v)
indicating a spike; otherwise, ((v) 0. S(4 represents whether the neuron fires a spike at time step Eq. 22 defines the reset of the membrane potential, where H [t] and V(t) denote the membrane potential before and after spike generation at time step t. respectively.
To enhance membrane potential awareness across inter-module connections within the S M-Forrer, we propose the CPLIF neuron. The membrane potential update rule for CPLIF can refer to Eq. (T). which extends the standard PLIF by assigning learnable membrane time constant to each individual channel. This design enables finer control over temporal dynamics at each discrete time, allowing more expressive and adaptive modeling. Here, n|c] is the softmax activation function to perceive channel interaction, and also ensures that ল. E (1, co). The firing and reset equations of the CPLIF
neuron are the same the Eq. 21 and Eq.
22 of the standard LIF neuron.
A.3 Datasets
1) KUL 19 46]:
The dataset consists of 64-channel EEG recordings from normal-hearing
subjects, collected using BioSemi ActiveTwo system at a sampling rate of 8192 Hz. Each subject was instructed focus on one of two simultaneous speakers. The auditory stimuli, consisting of four Dutch short stories narrated by three male Flemish speakers, were presented under two conditions:
15

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Table 7: Implementation details for KUL, DTU, and AV-GC datasets.
Datasets
KUL
DTU
AV-GC
Training Epochs
300
Batch Size
32 (Subject-dependeut setting) / 128 (Subject-independen setting)
Optimizer
Adam
Learning Rate (Within-trial)
lc-3
5c-4
5c-4
Learning Rate (Cross-trial)
2c-4
Learning Rate (Cross-subject)
2c-3
Weight Decay
le-2
Spiking Neuron
LIF (r=2.0, Vihreshold= 1.0) CPLIF (r=2.0, Vihreshoto=1.0)
Time Steps
4
Surrogate Function
Atan (a=5.0)
LR Scheduler
Cosine Annealing WarmRestarts
Seed GPUs
200
RTX 4090
dichotic (dry) presentation, with one speaker per ear, and head-related transfer function (HRTF) filtered presentation simulating speech from 90º to the left right. The stimuli were delivered through in-ear headphones, filtered at 4 kHz, and presented at 60 dB. Each subject participated in trials, each lasting minutes, for a total of 12.8 hours of EEG data.
The dataset contains 64-channel EEG recordings from 18 normal-hearin subjects, collected using& BioSemi Active'wwo system at sampling rate of 512 Hz. Each subject was instructed to focus on one of two simultaneous speakers, who narrated Danish audiobooks through ER-2 earphones set at 60 dB. The audiobooks, narrated by three male and three female speakers, were presented at a 60" angle relative to the subject's 's frontal position. The auditory stimuli were presented in a mixed speech format with varying reverberation levels. Each subject completed 60 trials, each lasting 50 seconds, resulting in a total of 15 hours of EEG data.
2) DTU 20 4
3) AV-GC-AAD 21 41 48] The audiovisual, gaze-controlled auditory attention dataset consists of EEG recordings from 16 heariig subjects who focused on one of two competing talkers located at + 90" relative to the subject. EEG data were recorded using 64-channel BioSemi ActiveTwo system. The auditory stimuli, consisting of Dutch science podcasts, were presented through insert earphones using HRTF to simulate spatial separation. The experiment involved 4 conditions- no visuals, static video, moving video, and moving target noise- each with 2 trials lasting 10 minutes. Detailed information on each condition is available in Rotaru et al [21] The visual setups varied to explore the effect of gaze on auditory attention, with the to-be-attended speaker switching sides after 5 minutes to simulate spatial attention shift. EEG data from subjects #2, #5, and #6 were excluded due to a lack of consent for public sharing. Subjects #1 and #3 were further excluded due to missing condition 4 recordings, leaving 11 subjects for analysis. For the cross-trial setting, a controlled paradigm with four conditions is used: conditions I (auditory-onlly static/moving videos) for training, and condition 4 (incongruent moving- target noise) for testing. This setting evaluates model robustness against cross-modal conflicts by examining domain shifts between audiovisual-relan (training) and mismatched (testing) conditions, and analyzing how audiovisual congruency impacts generalization in AAD models.
and 2-3 (audio-visuse)
A.4 Experimental Setup
We use the Adam optimizer with a learning rate to minimize the cross-entropy loss, setting the batch size to 32 (or 128 for subject-independent setting) and training for 300 epochs, more details are shown in the Table The hidden dimension D in S2 MMForreer is set to 8. An early stopping criterion applied if no sigmificant improvement the loss function is observed over 25 consecutive epochs, automatically halting training. The best model is saved based on both validation loss and accuracy, and the model with the best performance loaded for final evaluation. To ensure fairness, our proposed model and all reproducible baselines are trained following their originally reported optimization strategies, including the specific choice of optimizer, learning rate, and related hyperparameters. In addition, the same random seed is applied across all experiments to ensure reproducibility and
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Mengriitenn Dag Nedee Outko
Renge wire-355
Punce nive Neeer Oetle
1.0
1.0
0.9
0.9
0.9
Accuracy (%6
Accuracy
0.8
0.8
Accuracy
0.8
0.7
0.6
0.6
0.5
Former DEPNot DARN
0.5
5
DARkit
0.5
5 Former DBffect
DARNeCt
Dataset
Figure 4: Visualization comparison across all subjects on three datasets under within-trial settings.
fairness. All models are constructed and implemented using the PyTorch and SpikingJelly [30 frameworks, and all experiments conducted NVIDIA GeForce RTX 4090 GPU.
A.5 Conversion Steps from S M-FForrr SM-Former
To enable fair and controlled comparison between spiking and non-spiking models, we construct a matched ANN counterpart to our proposed MrM-Former, termed SM-Former. SM-Former retains the overall architectural structure, layer depth, and parameter budget of M---Foreer, while systematically replacing all spike-driven components with their standard ANN analogues. The conversion steps detailed below:
CPLIF Neuron Removal:
All channel-wise parametric PLIF (CPLIF) neurons removed, eliminating inter-module temporal spiking dynamics.
Branch Encoder Conversion: In both the spatial (SBE) and frequency (FBE) branches, all spiking neuron layers (e.g., LIF-Conv-BN) are replaced by ANN layers (e.g., Conv-BN-ReLU).
SCSA Replacement: The Spiking Channel Self-Attention (SCSA) module replaced with standard ANN-based channel-wise attention, employing QKV projections and softmax operations.
SMSC Conversion: All spiking preprocessing blocks in the Spiking Multi-Scale Convolution
(SMSC) module are replaced with conventional convolutional layers, such as DWConv-Bil -N-o--B--ReLL...
SGCM Conversion: the Spiking Gated Channel Mixer (SGCM), blocks like LIR-Conv-BN and LJF-DDWCnN-BN-LIIF replaced with Cony-BN-ReLU and WWWCoon--N--ell. respectively.
The Membrane Potential-ave -weet Token Mixer (MPTM) retains its structure but eliminates all spiking operations, replacing spike-gating and temporal fusion mechanisms with standard ANN counterparts.
MPTM Simplification:
This conversion preserves the representational capacity and computational structure, ensuring that performance differences between M-rmerr and SM-Former arise from the use of spike-driven versus non-spiking operations, rather than from architectural or parameter count differences.
A.6 Comprehensive Results
Figure. 5 presents a comparative statisties of model performances under within-trial and cross- trial settings. An overarching trend emerges from both settings: dual-branch architectures (e.g.. DBPNet, M-DBPNet, and M-M-Frrr consistently outperform single-branch models (e.g.. MBSSFCC and DARNet), highlighting the strength of leveraging multiple feature representa- tions in AAD tasks. the within-trial scenario (Figure. 5p), MrM-Former achieves the high- est decoding accuracy in 4 out of 9 conditions, outperforming DBPNet [6] and DARNet (5), which each lead only 2 conditions, indicating its superior performance on subject-seen data across all datasets. Moreover, in the more challenging cross-trial setting (Figure. 5b), S Former exhibits clear superiority, attaining top-1 accuracy in out 9 conditions, 3.5 times many all competing models combined, highlighting its strong generalization across subjects.
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Table 8: Ablation study and compare with recent SNN models on KUL dataset.
Feature Embeddings
Param (M)
Time Steps
Within-trial
Cross-trial
Methods
1s
2s
1s
S'M-Former (proposed)
CSP+DE
0.06
93.71 8.14
8.66
72.39 25.21
71.22 25.97
SM Former (ANN)
CSP+DE
0.06
91.38 9.71
90.65 10.80
69.46 + 23.75
66.69 + 23.52
CPLIF - LIF
CSP+DE
0.06
92.77 a: 9.01
91.60 a: 9.47
71.23 a 24.37
69.50 a: 25.24
SGCM MPTM
91.99
70.55
Spatial Branch
CSP
0.04
90.91 9.22
89.68 10.01
69.87 + 24.20
67.79 a 25.06
Frequency Branch
DE
0.01
89.15 10.54
88.90 10.03
70.18 a. 22.09
68.09 a: 21.42
QKFormer
0.29
85.42 11.36
84.87 = 12.82
96.96. + 20.97
65.24 23.36
Spike-driven Transformer 35
DE
0.37
86.24 11.20
84,81 13.51
64.94 + 22.78
64.83 + 21.17
Spikformer 34
DE
0.37
83.16 12.36
82,90 +: 13.46
64.11 + 22.00
63.06 + 22.79
Table 9: Ablation study and compare with recent SNN models AV-GC dataset.
Feature Embeddings
Param (M)
Time Steps
Within-trial
Cross trial
Methods
2s
1s
2s
1s
M-F-Frrrer (proposed)
CSP+DE
0.06
6/4
91.83 + 6.66
89.24 7,59
70.64 = 18.65
65.77 = 15.58
SM-Former (ANN)
CSP+DE
0.06
90.66 +
88.07
68.83 17.50
66.52 13.75
CSP4DE
0.06
7.57
16.03
w/o SGCM & MPTM
CSP+DE
0.05
6/4
90.72 1 6.63
88.03 7.45
68.84 16.24
64.51 + 15.90
Sputial Branch
CSP
0.04
6/4
89.12 7.31
8.43
68.02 15.00
63.85 14.55
Frequency
+
62.25 15.41
QKFomer 39
DE
0.29
6/4
77.87 =
7.890
63.54 13.39
60.20 13.02
Spike-driven Transformer
35
DE
0.37
6/4
80.15 + 7.60
78.49 7.85
62.53 15.34
59.48 : 11.98
Spikformer
DE
0.37
6/4
75.05 + 7.66
73.09 = 6.91
64.16 14.69
63.32 = 14.23
Remarkably, M- achieves this with only 0.06M
parameters, significantly outperforming larger mod- els. such as MBSSFCC (15] (16.8 (16. and DBPNet (0.88M), thereby demonstrating exceptional parameter efficiency. These results collectively reinforce the ef- fectiveness of S M-Former in delivering robust and ef- ficient AAD solutions. Moreover, while DBPNet's top-1 count drops from 2 (within-trial) to O (cross- (cross-trial), M- DBPNet 7 maintains stable performance and outper- forms DBPNet under cross-trial evaluation, corroborat- ing prior findings on stronger generalization ability.
Parformance dtatieties
Third
MBSSPCC
DARNE:
DBPNet M.D6PMcc 5'N-Forme
(a) Within-trial
We further provide the visualization analysis across all subjects on three datasets under within-trial settings, as shown in Figure. In terms of this setting, we found
Performance statictcs
that the distributions of decoding accuracies across all
models and datasets are relatively concentrated, so we
adopt the conventional 1.5 IQR rule which can effec- tively distinguish true variability from extreme outliers without excessively compressing the distribution range, thereby preserving the subtle differences among meth- ods while avoiding misleading impressions stability. Consistent with findings under the cross-trial setting, our M-F-rrr exhibits the fewest outliers, particularly demonstrating its advantage under the 1-second and 2-second decision windows and DTU dataset. We further supplement the results with a comparison across all subjects on the DTU dataset under the cross trial setting, shown in Figure. 6 For the vast majority of methods, the decoding accuracy of all subjects exceeds the chance level. Notably, consistent with the observations in Figure. our method exhibits fewer outliers compared other baselines, demonstrating superior robustness and generalization capability. A phenomenon worth discussing is that the performance degradation from within-tris to cross-trial the DTU dataset is less severe compared that ob- served on the KUL and AV-GC datasets. We attribute this to the larger number of trials per subject in the DTU dataset, which significantly enhances training stability and improves testing robustness under the zero-shot condition. In contrast, datasets with fewer trials per subject are more prone to poor generalization, potentially resulting in below-chance performance on the test set.
MRSSECE
DARNe:
BBPNet
N-D0PMet
5'N-Tormer
Figure 5: Performance statistics across all models under within- and cross-trial.
over-
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Eay Sa
Nary Kae
70.44%
Acurace (M)
12.00%
thingg Rara thi
Aoursey Thi
AcuunT ONL
1.30
123
72
1000
1000
Steps
(a) KUL Dataset
(b) DTU Dataset
(c) AV-GC Dataset
Figure 8: Visualization of the accuracy and firing rate change with the number of time steps Ts. where the blue line is the accuracy and the red line is the firing rate.
We conducted comprehensive ablation studies the KUL dataset, as shown in Table 8 The proposed S Former consistently outperforms (SM-Former) across all settings, demonstrating the ad- vantage of our spike-driven design. For example, under the 2-second within-trial condition, MrM-Former achieves 93.71% (SD: 8.14), surpassing S.M-oormerss 91.38% (SD: 9.71). Replacing the CPLIF neurons with standard LIF leads to consistent performance drops, validating the ben- efit of channel-wist adaptive time constants (e.g.. 71.22%
ANN counterpart
0.9
0.7
Accuracy
0.6
0.5
-+ 69.50% under cross-trial). Removing SGCM and MPTM further degrades results, highlighting their com- plementary roles in biologically plausible token-wise and thannel-wise modeling. Branch-level ablations show that Spatial branch performs better within-trial settings (e.g., 90.91% 1% vs. 89.15%) while the Frequency branch more effective in cross-trial scenarios (c.g.. 70.18% vs. 69.87%). Notably, despite having only 0.01M parameters, the Frequency-branch alone matches or exceeds recent SNN models such as QKFormer [39], Spike- Transformer [35), and Spik- former 33 (e.g., 89.15% vs. 85.52%, 86.24% and 83.16%), with more than 29 fewer parameters (0.01 vs. 0.29M). Ablation results on the AV-GC dataset (Table[) show generally consistent trends. Two exceptions are observed: (1) SM-Formes slightly outperforms S M-Former under the 1-second cross-trial condition; and (2) the Spatial-brande consistently outperforms the Frequency-brance across all settings, aligning with observations from the DTU dataset. The above analyses further support the robustness of M and the generalizability of its key components across datasets.
S'M.Fomer M-DEPNet
DEPNet
DARNet
Figure 6: Comparison across all subjects on the DTU dataset under cross-trial.
We evaluated the effect of time steps model per- formance under the cross-trial setup with a 2-second decision window. Figure. shows the classification accuracy and corresponding average firing rate across all subjects for three datasets, with time steps extend- ing from 1 to 8. Specifically, MrM-Former achieves the highest accuracy of 72.39% with a firing rate of 4.8% on the KUL dataset, 76.25% with 1.3% on DTU, and 70.64% with 1.4% AV-GC. With the initial in- crease in time steps, the accuracy generally improves (c.g.. from 4 time steps on KUL, to 4 time steps on DTU, and to 4 time steps on AV-GC), However, beyond 4 time steps, the performance gain becomes marginal, while the cumulative firing rate continues to rise, which is particularly evident on the KUL and AV-GC datasets. This observation suggests that fur- ther increasing the temporal resolution yields limited improvements in accuracy but incurs additional com- putational cost. Therefore, we choose time steps as trade-off, striking favorable balance between performance and energy efficiency.
1.0
0.8
0.7
Accuracy
0.5
0.3
SC3A
wie SMSC
1M Formor
0.1s
2s
Time Window (s)
Figure 7: Ablation studies SCSA and SMSC modules on DTU dataset under within-trial.
19

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Figure. 7 presents the ablation results of the hierarchical representation learning modules in S block, consisting of SCSA for modeling long-range global dependencies and SMSC for capturing local -ra-iga patterns. Even when either module is removed, the subsequent SGCM and MPTM layers are retained to ensure meaningful cross-branch complementary fusion learning. Under the shortest time window (0.1s), SCSA (60.79- = 4.23) outperforms SMSC (60.19 + 4.60). However, the combination of SCSA and SMSC significantly boosts accuracy to 75.84 + 5.46, yielding a 15.05% improvement over SCSA alone, highlighting the comprehensive strengths of global and local representations. At the Is window, SMSC slightly outperforms SCSA (78.51 + 6.64 vs. 78.01 + 6.93), suggesting that temporal context extends, modeling local dynamics becomes increasingly important. Their integration further boosts performance to 82.87 + 6.92. At the 2s window, SMSC shows advantage over SCSA (81.54 + 6.66 vs. 80.44 5.47). Combining both modules achieves the highest accuracy of 85.28 # 5.61, improving by 3.74% over SMSC and 4.84% over SCSA, validating the effectiveness of our S M-F-rrr architecture.
To further assess the energy efficiency and dynamic characteristics of S M-Fomeer, we analyze the av- erage spike firing rates within its spatial branch, frequency branch, and fusion module (SGCM &
0.00
0.7
Spat
0.06
Prequency
Average Spike finng
MPTM) under cross-trial settings on three datasets (Figure. 9 All components exhibit low firing rates (<0.08), confirming the model operates with the sparse nature of spiking activations, which mini- mizes computation and improves overall efficiency. Among the branches, the spatial branch generally exhibits the highest firing rates, consistent with ab- lation results (Table [ and9). where spatial cues contribute more substantially to performance in most conditions. Interestingly, on the KUL dataset with 2s window, the frequency branch surpasses the spatial branch in firing activity. This corresponds with its performance under this setting and suggests that M-Fomer can adapt the firing behavior of its branches in response to the informative- ness of spatial or frequency cues the input dynamically. As decision windows extend, firing rates rise due to increased input information, yet sparsity is still preserved. Notably, the fusion module consistently shows the lowest firing rates yet remains crucial for performance (per ablation studies). This phenomenon suggests that the fusion module can effectively coordinate and integrate abstracted representations from branches with minimal spiking activity. Such a mechanism might mimic the sparsely active characteristics observed in integration layers [49] of biological neural systems.
0.04
0.03
0.02
0.01
AV-GC
Figure 9: Firing rate analysis of M-M-Former across all datasets.
A.7 Theoretical Calculation of Energy Consumption
For ANNs, the theoretical energy consumption calculated by multiplying the total number of multiply-acuumulte (MAC) operations by the energy per MAC operation on specified hardware. Using the fvcore library 50 to compute floating-pone point operations (FLOPss, the energy consumption
can be expressed as:
EANN EMAC
FLOP
(23)
where FL.OP denotes the number of MAC operations in layer I, and FM = 4.6pJ represents the energy cost per MAC operation on 45mm hardware [51].
For SNNs, energy consumption involves both MAC operations and spike-driven accumulate (AC) The number of synaptic operations (SOPs) calculated as:
operations 31 52
SOP fr FLOP
(24)
where frl is the firing rate of spiking neuron layer 7 1, which can be computed as:
N
(0),
(25)
Tx N
20

---

where T' is the total number of time steps, N is the number of neurons in layer I - 1, and (t) denotes the spike output (0 or 1) of the i-th neuron at time step t. FLOP refers to the equivalent MAC operations layer and SOP is the number of spike-based AC operations (SOPs). Assuming the MAC and AC operations are performed on the 45nm hardware [5I] the consumption of the spiking transformer can be calculated as follows:
E = EMAC (FLOPC + EAC
SOP
Conv
Σ j=1
SOP SS S8A
(26)
where SOPCone represents the SOPs of convolution linear layer, and SOPsSA represents the SOPs of an SSA module, FLOP Con:: represents the FLOPs of the first layer before encoding input frames into spikes. N is the total number of convolution layers and linear layers, and M is the number of SSA modules. During model inference, several cascaded linear operation layers such as convolution, linear, and BN layers, can be folded into one single linear operation layer [39,, still enjoying the Aype operations with a spike-form input tensor.
The energy consumption calculation for our M-Forrr also follows Eq. 26 Specifically, the MAC operations are primarily generated by the first convolution layer of the cranch-specific spiking encoders SBE and FBE, while the remaining parts involve SOP calculations.
A.8 Future Work and Limitation
is lightweight and low-power spiking neural network tackle AAD tasks, which has demonstrated its effectiveness through comprehensive experiments. Notably, it utilizes only 0.06M parameters, significantly outperforming recent network models in terms of parameter efficiency. In future research, we aim to further unlock the full potential and scalability of M-r-Frrr Additionally, SNNs are known for their spatial-temporal dynamics. In our solution, S M-Forer achieves SOTA feature-oriented representation inter-mixing, but the utilization of CSP and DE features inevitably disrupts the inherent temporal dynamics. In future work, we are committed to exploring how to model the temporal dynamics inherent in EEG data.
Our proposed
Moreover, SNNs are particularly appealing for their compatibility with neuromorphic hardware due to their sparse and event-driven nature. In line with recent influential SNN studies in other realms (e.g., CV, NLP and Speech), we have reported the theoretical energy consumption of M- and compared it against other SNN-based approaches. This analysis further validates the energy efficiency of our model. To bridge algorithm and hardware co-design, we are currently conducting hardware simulations of S M-Formrr on brain-inspired neuromorphic platforms. This line of work will help validate the real- world deployment potential of our model on dedicated low-power chips.
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Reviews:
[{"review_id": "oVPxptKxGD", "reviewer": "Reviewer_Ypre", "summary": "This 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.", "questions": "- 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?", "limitations": "yes", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**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.", "was_revised": true, "final_justification": "The rebuttal partially clarifies the methodological aspects, but the core issues remain. First, the application of SNNs to EEG processing is well-established in the literature [1-3], and the proposed method does not demonstrate significant novelty. Second, the scope is limited to Auditory Attention Detection, which restricts the broader impact of this work.\n\nFurthermore, the power consumption concern, while acknowledged, is not a primary bottleneck in EEG processing. The field already has numerous lightweight models (e.g., ShallowConvNet, EEGNet) with minimal power requirements. Therefore, the claimed advantages in this aspect are not compelling enough to justify the incremental contribution.\n\nGiven these considerations, I maintain my original evaluation.\n\n[1] Kumar, Neelesh, et al. \"Decoding eeg with spiking neural networks on neuromorphic hardware.\" Transactions on Machine Learning Research (2022).\n\n[2] Antelis, Javier M., and Luis E. Falcón. \"Spiking neural networks applied to the classification of motor tasks in EEG signals.\" Neural networks 122 (2020): 130-143.\n\n[3] Gong, Peiliang, et al. \"A spiking neural network with adaptive graph convolution and LSTM for EEG-based brain-computer interfaces.\" IEEE Transactions on Neural Systems and Rehabilitation Engineering 31 (2023): 1440-1450.", "rebuttal": "Dear Reviewer Ypre,\n\nThank you for your detailed comments. We genuinely appreciate your insights and will address your concerns point by point below.\n\n### **[Weakness]:**\n\n**1. While the application of SNNs to AAD is interesting, the methodological novelty appears limited ...**\n\nWe believe our work introduces **S²M-Former**, a fundamentally **unified, symmetric spike-driven framework** specifically tailored for dual-feature EEG-based AAD, advancing beyond prior SNN and ANN approaches. \n\nUnlike recent methods that **treat branches independently and rely on weak or late-stage feature fusion**, S²M-Former innovatively leverages intra-branch modules for **hierarchical feature learning** and the shared inter-branch fusion module for **synergistic integration**, ensuring both accuracy and efficiency.\n\nAs also recognized by Reviewer aRKz, our S²M-Former is the first spiking framework to hierarchically integrate spatial-temporal and topographic EEG features, rather than relying on simple feature concatenation.\n\nImportantly, the inherently sparse and binary nature of spike representations poses a key challenge that has limited recent SNNs from matching ANN performance on AAD tasks (see *Related Work*). Our model outperforms recent SOTA ANN counterparts (e.g., DBPNet, M-DBPNet) on multiple AAD benchmarks, while using 14.7× fewer parameters and achieving 5.8× lower theoretical energy consumption. This overcomes **the long-standing challenge that existing SNNs trade accuracy for efficiency** and opens a new direction for high-performance, low-power AAD.\n\nIn this sense, our work contributes not only an effective AAD solution, but also a novel demonstration that **spiking neural architectures can scale to dual-feature, high-accuracy EEG decoding**, moving beyond the limitations of previous SNN applications in this domain.\n\n**2.  The hybrid design that combines elements from both SNNs and ANNs ...**\n\nWe would like to clarify a potential misunderstanding regarding \"hybrid design\". S²M-Former is implemented entirely under a purely spike-driven computation paradigm. Specifically, all internal layers perform with **intra-module computation** via LIF neurons, while **inter-module communication** is handled by CPLIF neurons (explicitly described in **Section 3.1, line 125-127** on Page 3).\n\nComponents such as convolution and linear layers are fundamental to representation learning and widely used in both ANN and SNN models, their presence does not imply a hybrid architecture [1]. The **key factor** that determines whether a model is hybrid ANN-SNN or pure SNN lies in whether its internal computation and communication are truly spike-driven [2]. As such, S²M-Former performs internal computation **only when spikes are fired**, relying on accumulate operations rather than floating-point based multiply-accumulate operations. \n\nThis enables full compatibility with neuromorphic platforms such as Intel Loihi or Tianjic, which support event-driven execution. We have already converted the trained weights of S²M-Former into a format compatible with Intel Loihi-2 [3] by leveraging LAVA framework, and we are actively pursuing access to neuromorphic hardware for evaluation. We will incorporate this clarification in the final version of the paper.\n\n[1] Eshraghian J K, et al. Training spiking neural networks using lessons from deep learning [J]. Proceedings of the IEEE, 2023.\n\n[2] Kudithipudi D, et al. Neuromorphic computing at scale [J]. Nature, 2025.\n\n[3] Orchard G, et al. Efficient neuromorphic signal processing with loihi 2 [C]. 2021 IEEE Workshop on Signal Processing Systems (SiPS), 2021.\n\n**3. Table 5 shows only moderate reductions in theoretical energy consumption ...**\n\nThank you for this valuable observation. We would like to clarify the context behind our energy-efficiency claims.\n\nAs shown in **Table 2, 3, Fig. 4 and 5**, dual-branch models consistently outperform single-branch ones, supporting the hypothesis (Page 2, Lines 38–55) that complementary learning between spatial-temporal and topographic EEG features enhances decoding performance. However, most existing dual-branch ANN-based AAD models (e.g., DBPNet, M-DBPNet) rely on **isolated** feature learning paradigms, with each branch built upon distinct architectures. This separation not only limits the ability to learn cross-domain dependencies but also requires **customized feature extractors** (e.g., deep 3D CNN blocks), leading to substantially higher parameter counts, computational cost, and energy consumption.\n\nIn contrast, S²M-Former adopts a **unified, symmetric spike-driven architecture**, where both branches operate on lightweight 1D token sequences, leveraging efficient intra-branch modules for feature learning and the shared inter-branch fusion modules for synergistic integration. This design supports strong representational capacity with minimal overhead, achieving up to **14.7× fewer parameters** and **5.8× lower theoretical energy** while outperforming DBPNet and M-DBPNet (Table 5).\n\nTherefore, we believe **the observed energy reductions** are **substantial and meaningful**, especially when **contextualized against high-performing dual-branch models**. S²M-Former offers a favorable performance-energy trade-off that is critical for real-world, low-power AAD deployment.\n\n**4. The writing and presentation of the method could be improved.**\n\nThank you for the helpful feedback. While we have rigorously defined all variables and detailed equations (Eq.(2)–Eq.(18)), we acknowledge that parts of the model description may still lack intuitive explanation. To address this, we will refine the text and more clearly align symbolic expressions with Fig. 2 to enhance presentation in the final version.\n\n### **[Questions]:**\n\n**1. Why energy consumption is a critical factor in the context of AAD ?**\n\nThank you for raising this important point. AAD systems are widely recognized as crucial components of neuro-steered hearing technologies, such as **smart hearing aids [1], wearable EEG headsets [2], and earable BCIs [3]**, which are typically battery-powered and constrained by power, latency and computational resources, particularly in dynamic, real-world conditions.\n\nIn this context, energy-efficient AAD models are vital to ensure **long battery life, user comfort, and practical deployment.** As highlighted in several studies [1–3], current AAD systems are often too resource-intensive for such applications. Our S²M-Former addresses this with a lightweight spike-driven architecture that significantly reduces computational cost.\n\nFor instance, a neuro-steered hearing aid that continuously decodes a user's attentional focus to selectively amplify the attended speaker in noisy environments. Such a device must operate reliably for hours on limited hardware and battery. In such scenarios, the lightweight, spike-driven design of S²M-Former is especially impactful, not only due to its low computational complexity, but also for its event-driven nature, **where computations are triggered only by discrete spikes.** **This asynchronous paradigm** better aligns with EEG dynamics in long-term monitoring, minimizing power consumption and facilitating efficient deployment in wearable AAD systems.\n\nThis clarification will be included in the final paper.\n\n[1] Van Eyndhoven S, et al. EEG-informed attended speaker extraction from recorded speech mixtures with application in neuro-steered hearing prostheses [J]. IEEE Transactions on Biomedical Engineering, 2016.\n\n[2] Mirkovic B, et al. Decoding the attended speech stream with multi-channel EEG: implications for online, daily-life applications [J]. Journal of Neural Engineering, 2015.\n\n[3] Fiedler L, et al. Single-channel in-ear-EEG detects the focus of auditory attention to concurrent tone streams and mixed speech [J]. Journal of Neural Engineering, 2017.\n\n**2. Would an equivalent ANN with similar inductive biases potentially perform even better ?**\n\nActually, we **have already examined this** in our main paper by designing a matched ANN counterpart, termed **SM-Former**, where all spike-driven modules in S²M-Former are replaced with standard ANN components while preserving the overall architecture and parameter count. (Please refer to our response to **Reviewer aRKz – Q1** for the detailed SNN-to-ANN conversion procedure.)\n\nAs shown in **Tables 4, 8, and 9** of the main paper (reproduced below for clarity), S²M-Former consistently outperforms SM-Former across most datasets and evaluation settings, demonstrating that the performance gain stems not just from architectural design, but from the **spike-driven computation paradigm itself**.\n\nTable 1: Comparison with SM-Former on Three AAD Datasets.\n\n| Dataset | Model      | Within-trial (2s) | Within-trial (1s) | Cross-trial (2s)  | Cross-trial (1s)  |\n| ------- | ---------- | ----------------- | ----------------- | ----------------- | ----------------- |\n| DTU     | S²M-Former | **85.28 ± 6.01**  | **82.87 ± 6.92**  | **76.74 ± 9.96**  | **75.75 ± 9.96**  |\n|         | SM-Former  | 80.94 ± 5.66      | 80.30 ± 6.77      | 73.49 ± 8.09      | 73.48 ± 7.98      |\n| KUL     | S²M-Former | **93.71 ± 8.14**  | **92.27 ± 8.66**  | **72.39 ± 25.21** | **71.22 ± 25.97** |\n|         | SM-Former  | 91.38 ± 9.71      | 90.65 ± 10.80     | 69.46 ± 23.75     | 66.69 ± 23.52     |\n| AV-GC   | S²M-Former | **91.83 ± 6.66**  | **89.24 ± 7.59**  | **70.64 ± 18.65** | 65.77 ± 15.58     |\n|         | SM-Former  | 90.66 ± 6.84      | 88.07 ± 7.10      | 68.83 ± 17.50     | **66.52 ± 13.75** |\n\nThese results clearly show that S²M-Former not only retains the inductive biases of its ANN counterpart, but also benefits from the temporal dynamics, event-driven, and biological plausibility offered by spiking computation. This validates our core motivation: **spike-based processing can achieve superior performance while being more energy-efficient.**\n\nWe will make this point more prominent and explicit in the final version."}, {"review_id": "q4bt1V8bJ5", "reviewer": "Reviewer_qaAt", "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.", "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.\n2) Is the CSP (Common Spatial Pattern) process applied before or after dataset partitioning.\n3) 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?\n4) 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.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "# Strength\n+ The paper proposes a novel spike-driven symmetric architecture for auditory attention decoding.\n+ While achieving state-of-the-art performance across three benchmark datasets, the method maintains minimal computational cost, reporting only 0.06M parameters.\n+ The paper provides comprehensive and extensive experiments, including evaluations under within-trial, cross-trial, and cross-subject settings, as well as detailed ablation studies.\n# Weakness\n+ Some definitions are unclear or missing. For example, DWConv and PWConv.\n+ Although the authors emphasize the importance of reducing computational cost, it's unclear why this method matters.", "was_revised": true, "final_justification": "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. Besides, The authors have addressed most of my concerns and answered the key questions during rebuttal. But the method and motivation in the current draft are not clearly written. I suggest the authors consider rephrasing this part in the final version to improve clarity. Therefore, I have increased my socre from 3 to 4.", "rebuttal": "Dear Reviewer qaAt,\n\nThank you for the detailed comments and insightful suggestions. We genuinely appreciate your insights and will address your concerns point by point below.\n\n### **[Weakness]:**\n\n**1. Some definitions are unclear or missing. For example, DWConv and PWConv.**\n\nThank you for pointing this out. We apologize for the omission and will clarify the definitions in the final version. Specifically:\n\n- DWConv refers to depthwise convolution, which handles each channel separately with its unique filter.\n- PWConv refers to pointwise convolution, i.e., a 1×1 convolution used to project features across channels.\n\nWe will revise the descriptions accordingly, such as:\n\nLine 188 of page 5: \"..., followed by point-wise convolution **(PWConv)** and batch normalization.\"\n\nLine 190 of page 5: \"..., and each part is passed through a **depthwise convolution** (DWConv1d$_{k_i}$) with ...\"\n\n**2. Although the authors emphasize the importance of reducing computational cost, it's unclear why this method matters.**\n\nThank you for the thoughtful question.  AAD is increasingly being integrated into low-power neuro-steered devices, such as **smart hearing aids [1], wearable EEG headsets [2], and earable BCIs [3]**. These systems are inherently battery-powered and face strict constraints on energy, latency and processing efficiency, especially under continuous operations in real-world environments.\n\nFor instance, a neuro-steered hearing aid that continuously tracks a user's attentional focus in noisy environments requires energy-efficient, lightweight models for long-term usability. In such scenarios,  **S²M-Former** is particularly advantageous, as it achieves higher decoding accuracy while significantly reducing **parameter count and theoretical energy consumption** compared to recent ANN-based dual-branch models (e.g., DBPNet, M-DBPNet), as shown in Table 5.\n\nThis efficiency stems not only from a **unified and symmetric architecture** that processes lightweight 1D token sequences without requiring complex branch-specific designs (e.g., deep 3D CNN blocks for topographic features), but also importantly from the **fully spike-driven, brain-inspired paradigm**, where internal computations are triggered **only when spikes (i.e., 1) are fired** via accumulate (AC) operations, unlike multiply-accumulate (MAC) operations requiring costly floating-point computations. This **asynchronous, event-driven computation** avoids unnecessary activity, making S²M-Former particularly well-suited for long-term monitoring, enabling low-power deployment in wearable AAD systems.\n\nWe will clarify and emphasize this point in the final version of the paper.\n\n[1] Van Eyndhoven S, et al. EEG-informed attended speaker extraction from recorded speech mixtures with application in neuro-steered hearing prostheses [J]. IEEE Transactions on Biomedical Engineering, 2016.\n\n[2] Mirkovic B, et al. Decoding the attended speech stream with multi-channel EEG: implications for online, daily-life applications [J]. Journal of Neural Engineering, 2015.\n\n[3] Fiedler L, et al. Single-channel in-ear-EEG detects the focus of auditory attention to concurrent tone streams and mixed speech [J]. Journal of Neural Engineering, 2017.\n\n### **[Questions]:** \n\n**1. How is the channel shuffle operation implemented in practice? A performance comparison with and without the channel shuffle.**\n\nWe adopt a group-wise **channel shuffle operation** (as discussed in Section 3.3.2 on Page 5) to enhance inter-group information exchange after multiple depth-wise convolutions. Given an input tensor of shape `[Ts, B, N, D]`  (as in Eq. (11) of the paper), where `D` is divisible by the number of groups `g`, we perform the following steps: 1. **Reshape** to `[Ts, B, N, g, D/g]`; 2. **Transpose** the group and channel dimensions; 3. **Flatten** back to the original shape `[Ts, B, N, D]`.\n\nThis process is implemented as:\n\n```\ndef channel_shuffle(self, x, groups):\n    \"\"\"\n    Ts: time steps, B: batch size, D: number of channels, N: number of tokens.\n    \"\"\"\n    Ts, B, N, D = x.size()\n    channels_per_group = D // groups\n    x = x.view(Ts, B, N, groups, channels_per_group) # (Ts, B, N, g, D/g)\n    x = x.transpose(3, 4).contiguous() # (Ts, B, N, D/g, g)\n    x = x.view(Ts, B, N, D)            # (Ts, B, N, D)\n    return x\n```\n\nImportantly, the shuffle operation is purely tensor rearrangement and is **identical during training and testing**, introducing no learnable parameters.\n\nTo assess its contribution, we are conducting ablation experiments by removing the **channel shuffle (CS)**.  Results across three datasets are summarized below:\n\nTable 1: DTU dataset comparison\n\n| Model          | Within-trial (2s) | Within-trial (1s) | Within-trial (0.1s) | Cross-trial (2s) | Cross-trial (1s) | Cross-trial (0.1s) |\n| -------------- | ----------------- | ----------------- | ------------------- | ---------------- | ---------------- | ------------------ |\n| **S²M-Former** | 85.28 ± 6.01      | **82.87 ± 6.92**  | **75.84 ± 5.46**    | **76.74 ± 9.96** | **75.75 ± 9.96** | **70.36 ± 7.31**   |\n| **w/o CS**     | **85.39 ± 6.27**  | 82.31 ± 6.60      | 75.83 ± 5.85        | 75.70 ± 10.38    | 75.28 ± 10.24    | 69.69 ± 7.58       |\n\nTable 2:  KUL dataset comparison\n\n| Model          | Within-trial (2s) | Within-trial (1s) | Within-trial (0.1s) | Cross-trial (2s)  | Cross-trial (1s)  | Cross-trial (0.1s) |\n| -------------- | ----------------- | ----------------- | ------------------- | ----------------- | ----------------- | ------------------ |\n| **S²M-Former** | **93.71 ± 8.14**  | **92.27 ± 8.66**  | **83.39 ± 12.80**   | **72.39 ± 25.21** | **71.22 ± 25.97** | **66.49 ± 21.03**  |\n| **w/o CS**     | 93.25 ± 8.81      | 92.01 ± 9.52      | 82.46 ± 13.36       | 71.51 ± 25.93     | 70.43 ± 25.60     | 66.29 ± 20.77      |\n\nTable 3:  AV-GC dataset comparison\n\n| Model          | Within-trial (2s) | Within-trial (1s) | Within-trial (0.1s) | Cross-trial (2s)  | Cross-trial (1s)  | Cross-trial (0.1s) |\n| -------------- | ----------------- | ----------------- | ------------------- | ----------------- | ----------------- | ------------------ |\n| **S²M-Former** | **91.83 ± 6.66**  | **89.24 ± 7.59**  | **74.42 ± 7.79**    | 70.64 ± 18.65     | **65.77 ± 15.58** | **65.49 ± 13.74**  |\n| **w/o CS**     | 91.18 ± 6.30      | 88.59 ± 7.35      | 74.26 ± 7.48        | **70.65 ± 18.47** | 65.60 ± 13.83     | 65.42 ± 15.64      |\n\nAs shown in Table 1-3, **removing channel shuffle leads to consistent drops** in accuracy under most settings. This supports its effectiveness in enhancing inter-group diversity. We will include full results and discussion for all decision windows and datasets in the final version.\n\n**2. Is the CSP process applied before or after dataset partitioning.**\n\nWe appreciate your attention to this detail.  CSP was applied **AFTER dataset partitioning.** As stated on **Page 7, Lines 254–255,**\n\n>\"**We ensure no overlap between sets after splitting and perform separate feature extraction within each set to avoid any potential information leakage.**\"\n\nThis process strictly avoids any potential information leakage from test data into the CSP feature extraction.\n\n**3. How was the 0.06M parameter count determined？**\n\nThank you for your question. We would like to clarify that the reported parameter count of **0.06M remains constant across all decision window lengths and datasets.**\n\nAs detailed in **Eq. (2)–Eq. (18)**, all components in S²M-Former, including branch-specific spiking encoders (SBE, FBE) and S²M Module (SCSA, SMSC, SGCM and MPTM), **contain learnable operations only through channel-wise projections (D)**.  Each component is accompanied by explicit tensor shape definitions (e.g.,  $\\mathbb{R}^{T_S \\times N \\times D}$), clearly illustrating that no learnable transformations along the token dimension (N), which corresponds to the decision window length in the spatial branch and the topographic map resolution in the frequency branch.  As such, varying the number of tokens does not affect the model’s parameter count.\n\nFor clarity, operations such as token dimension concatenation (Page 5, Lines 195–196), global average pooling (Eq. (15)), and max pooling (Eq. (18)) do not introduce additional parameters, and thus do not impact the overall parameter count. Moreover, as specified on Page 14, Line 578,\n\n>**\"The hidden dimension D in S²M-Former is set to 8.\"**\n\nWe set the hidden dimension to $D=8$ for all experiments, and all three datasets (KUL, DTU, AV-GC-AAD) employ a uniform 64-channel EEG configuration, ensuring consistent input dimensionality. Therefore, the reported **0.06M parameters** reflect a unified model configuration used across all datasets and settings. We will clarify this point more explicitly in the final version of the paper.\n\n**4. Did the authors use sliding window processing during training? Was the same strategy used across all methods for fair comparison?**\n\nThank you for the insightful question. **YES,** we **adopt the same sliding window strategy as prior works** to ensure fair comparison. As stated on Page 6, Lines 239–240,\n\n>**\"The pre-processed EEG data are segmented using a sliding window with 50% overlap, where the window step size is half the time window length.\"**\n\nThe **unified** data preprocessing pipeline is consistently applied across all datasets and all methods (including baselines and our model). To ensure **fair benchmarking**, we reproduce and evaluate all compared methods under the same implementation, including window length, step size, and overlap ratio. This setup follows **standard practice adopted in prior AAD works** (e.g., M-DBPNet, DBPNet, DARNet), ensuring that observed performance gains arise from model innovations rather than differences in data segmentation or preprocessing.\n\nTo avoid ambiguity and facilitate reproducibility, we will strengthen this clarification in the final version and release our code upon acceptance for transparent validation."}, {"review_id": "VyUWMFoYUA", "reviewer": "Reviewer_aRKz", "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.", "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.\n2. Which part of the proposed framework contribute most to capture the long-range dependencies?\n3. What might be the reason behind the narrowed advantage over prior non-SNN methods (e.g., DBPNet) in the KUL benchmark?", "limitations": "yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### Strengths: \n1. 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.\n2. The comparison and benchmark clearly shows the strength of the proposed model.\n3. Compared to prior works like Spikformer (SNN) and DBPNet (ANN), its improvement in efficiency is quite noticeable.\n### Weaknesses: \n1. Performance in terms of accuracy improvements is not consistent across different benchmarks.", "was_revised": true, "final_justification": "This paper mainly proposes a hierarchical integration of EEG and topographical maps for more robust auditory attention detection. The reported results on several benchmarks show notable improvements over prior works. During the rebuttal phase, the authors answer and clarify the questions raised by reviewers and make the paper much stronger by including the implementation details, computational efficiency, and analysis of the experimental results. Given the merit of the proposed method, I maintain a positive rating.", "rebuttal": "Dear Reviewer aRKz,\n\nThanks for your constructive comments. We will address your concerns point by point in the following. \n\n### **[Weakness]:**  \n\n**1. Performance in terms of accuracy improvements is not consistent across different benchmarks.**\n\nThank you for your valuable feedback. We acknowledge the performance variations observed across datasets and appreciate the opportunity to clarify.\n\nFirst, such variations may reflect **intrinsic differences across datasets**. As summarized in **Table 1**, the **KUL**dataset, **with fewer trials and longer recordings (8 trials × 360s)**, presents a relatively simpler decoding scenario and is more susceptible to **performance saturation**. In contrast, **DTU** (60 trials per subject with only 50s recordings) and **AV-GC-AAD** (audio-visual distraction with multi-condition) pose greater challenges due to increased inter-subject variability and realistic multisensory interference, thereby offering a more rigorous benchmark for model robustness.\n\nSecond, our goal is not to optimize for a single dataset, but to demonstrate generalizability, consistently strong performance, and efficiency across **diverse AAD conditions**. As discussed in Section *Results* (Page 7, Lines 257–271) and Appendix A.5 (Fig. 5 and its discussion; Page 15, Lines 588–613),  S²M-Former achieves the highest accuracy in **11 out of 18** test conditions and achieves top-3 performance in 83.33%, consistently outperforming dual-branch baselines (e.g., DBPNet, M-DBPNet) in terms of **cross-dataset reliability and robustness**.\n\nWhile S²M-Former does not always achieve the highest accuracy on KUL within-trial decoding (Table 2), its strength becomes more evident in **cross-trial setups**, where it reaches the best accuracy under 1s windows and competitive results under 0.1s (Table 3). More importantly, under the most rigorous **leave-one-subject-out (LOSO)** evaluation (Table 6), S²M-Former achieves the best performance, highlighting its strong generalization across subjects.\n\nImportantly, S²M-Former achieves this with only 0.06M parameters, offering a 14.7× parameter reduction and 5.8× lower theoretical energy cost compared to DBPNet  (Table 5), while maintaining or exceeding its performance. Even in benchmarks where accuracy margins are narrower (e.g., KUL), S²M-Former delivers **comparable accuracy with significantly better efficiency**, which is particularly valuable for **real-world, low-power applications**.\n\nWe will clarify this point further in the final version of the paper.\n\n### **[Questions]:**    \n\n**1. What is the difference between S²M-Former and SM-Former? Is it a simple deletion of spiking neurons?**\n\nThank you for your question. To clarify, **SM-Former is NOT merely a simple deletion of spiking neurons replacement**, but rather a **rational** ANN-based version of S²M-Former, where all spike-driven components are systematically replaced with their standard ANN counterparts, while preserving the overall architectural structure and parameter count.\n\nSpecifically:\n\n- We first remove all the proposed channel-wise parametric PLIF **(CPLIF)** neuron, thereby eliminating spike-driven temporal dynamics among inter-module.\n- In both **SBE** and **FBE** (branch-specific spiking encoders), all LIF neurons are replaced by standard ReLU activations.\n- The spiking channel self-attention **(SCSA)** module is replaced with a standard channel-wise self-attention, using typical QKV projections and softmax operations.\n- The spiking multi-scale convolution **(SMSC)** is converted into a  ANN conventions. All spiking neuron-based pre-processing blocks (e.g., $LIF$-$Conv$-$BN$) are similarly replaced by $Conv$-$BN$-$ReLU$.\n- In the spiking gated channel mixer (**SGCM** ) module,  spiking neuron-based pre-processing blocks (e.g., $LIF$-$Conv$-$BN$ and $LIF$-$DWConv$-$BN$-$LIF$) are converted to standard ANN forms, such as $Conv$-$BN$-$ReLU$ and $DWConv$-$BN$-$ReLU$.\n- The membrane potential-aware token mixer (**MPTM**) module fuses dual-branch token representations without any LIF neuron, as temporal spiking behavior is no longer present.\n\nAll modifications are carefully made to preserve the overall architecture, layer depth, and channel dimensions, ensuring that **SM-Former serves as a structurally aligned ANN counterpart**. This enables a fair assessment of whether spiking computations offer tangible benefits over ANN counterparts.\n\n**We will add this clarification and include a more detailed description of SM-Former in the final version of the paper.**\n\n**2. Which part of the proposed framework contribute most to capture the long-range dependencies?**\n\nThank you for the insightful question. The membrane potential-aware token mixer **(MPTM)** plays the most crucial role in modeling long-range dependencies within our framework. \n\nAs detailed in Section 3.3.4 on Page 6 and illustrated in Fig. 2(f) on Page 4, MPTM enables global context modeling through a **three-stage** process: core representation, refinement and fusion, and output generation.\n\nIn the **core** stage, global average pooling (GAP) condenses token-wise information into compact **global summaries**, allowing the model to aggregate contextual information across the entire sequence.\n\nIn the **refinement and fusion** stage, these summaries are proportionally broadcast back to the sequence and used to modulate local token features via spiking gating and residual fusion, thereby **injecting long-range contextual cues into each token.**\n\nFinally, in the **output generation** stage, the globally modulated features are concatenated with compressed representations obtained via max pooling, enhancing representation diversity and supporting **context-aware encoding**.\n\nThrough this **core → refine → fuse** process, MPTM facilitates global information integration and cross-branch interaction, making it a key contributor to the model’s capacity for capturing long-range dependencies essential for robust auditory attention decoding.\n\nWe will highlight this role more clearly in the final version of the paper.\n\n**3. What might be the reason behind the narrowed advantage over prior non-SNN methods (e.g., DBPNet) in the KUL benchmark?**\n\nThank you for this thoughtful question. While the performance margin of S²M-Former over prior ANN-based methods (e.g., DBPNet, M-DBPNet) appears narrower on the KUL benchmark, we ponder and believe this may stem from the **specific data characteristics** of KUL, rather than an inherent limitation of our model.\n\nKUL consists of only **8 long-duration trials (360s each)** under a **single, fixed auditory scene**, resulting in low trial diversity and highly repetitive EEG patterns. This **homogeneity** may limit the temporal variability that **spike-driven models are designed to exploit via their intrinsic temporal dynamics**. Moreover, the **within-trial setting**, where both training, validation and testing samples are drawn from the same trials, further reduces variability and encourages models to recognize trial-specific EEG patterns. Consequently, we believe the performance differences between models tend to narrow under this setting, not because S²M-Former lacks representational power compared to ANN-based methods, but rather due to the **low-complexity and low-variability** nature of the evaluation setup.\n\nTable 1: Within-trial results under KUL dataset (In all tables, **bold** indicates the best performance, and *italic* denotes the second-best.) \n\n| Models     | 2s               | 1s               | 0.1s             |\n| ---------- | ---------------- | ---------------- | ---------------- |\n| DBPNet     | 93.66 ± 7.88     | **93.25 ± 7.33** | *85.70 ± 9.75*   |\n| M-DBPNet   | **93.75 ± 6.34** | *93.19 ± 7.28*   | **86.16 ± 9.94** |\n| S²M-Former | *93.71 ± 8.14*   | 92.27 ± 8.66     | 83.39 ± 12.80    |\n\nHowever, the strengths of S²M-Former emerge more clearly in **cross-trial** and **leave-one-subject-out** evaluations under the KUL benchmark, where it matches or outperforms DBPNet and M-DBPNet in most cases (Table 2 and Table 3), demonstrating superior **generalization** under more challenging and realistic settings.\n\nTable 2: Cross-trial results under the KUL dataset\n\n| Models     | 2s                | 1s                | 0.1s              |\n| ---------- | ----------------- | ----------------- | ----------------- |\n| DBPNet     | *72.95 ± 24.36*   | *70.89 ± 25.01*   | 65.67 ± 21.84     |\n| M-DBPNet   | **74.27 ± 21.37** | 70.64 ± 23.82     | **66.97 ± 21.87** |\n| S²M-Former | 72.39 ± 25.21     | **71.22 ± 25.97** | *66.49 ± 21.03*   |\n\nTable 3: Leave-one-subject-out results under the KUL dataset\n\n| Models     | 2s                | 1s                |\n| ---------- | ----------------- | ----------------- |\n| DBPNet     | *74.82 ± 13.40*   | *71.77 ± 14.07*   |\n| M-DBPNet   | 72.83 ± 12.49     | 71.72 ± 13.35     |\n| S²M-Former | **75.75 ± 13.43** | **74.37 ± 12.57** |\n\nFurthermore, across more complex benchmarks such as **DTU (with more trials and shorter durations, specifically 60 trials × 50s )** and **AV-GC-AAD (audiovisual distractions with multi-condition)**, **S²M-Former** consistently outperforms prior models, confirming its effectiveness and **robustness across diverse AAD conditions**.\n\nWe will clarify these points in the final version of the paper."}]

Author rebuttal:

Meta-review: The paper presents S^2M-Former, a spiking symmetric mixing framework devised for auditory attention detection (AAD) using EEG signals. It adopts a symmetric architecture with spatial and frequency branches, along with lightweight 1D token sequences to replace conventional 3D operations. Unlike traditional methods that naively concatenate features, S^2M-Former uses hierarchical integration to fuse spatial-temporal EEG dynamics with brain topographical maps. The proposed approach obtains state-of-the-art classification performance across three publicly available AAD datasets while significantly reducing model size and energy consumption.

Strengths include:  spiking neural networks (SNNs) for EEG-based AAD, symmetric architecture, evaluation on three publicly available datasets, and state-of-the-art performance with reduced computational cost. Weaknesses include: moderate methodological novelty, modest and inconsistent performance gains over prior methods,   the need for elaborating on the model efficiency aspects. 

The rebuttal process helped shed light on the concerns.
Decision: Accept (poster)