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Zhao Lv

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17 papers

1 author row

AAAI Conference 2026 Conference Paper

BrainHGT: A Hierarchical Graph Transformer for Interpretable Brain Network Analysis

Jiajun Ma
Yongchao Zhang
Chao Zhang
Zhao Lv
Shengbing Pei

Graph Transformer shows remarkable potential in brain network analysis due to its ability to model graph structures and complex node relationships. Most existing methods typically model the brain as a flat network, ignoring its modular structure, and their attention mechanisms treat all brain region connections equally, ignoring distance-related node connection patterns. However, brain information processing is a hierarchical process that involves local and long-range interactions between brain regions, interactions between regions and sub-functional modules, and interactions among functional modules themselves. This hierarchical interaction mechanism enables the brain to efficiently integrate local computations and global information flow, supporting the execution of complex cognitive functions. To address this issue, we propose BrainHGT, a hierarchical Graph Transformer that simulates the brain’s natural information processing from local regions to global communities. Specifically, we design a novel long-short range attention encoder that utilizes parallel pathways to handle dense local interactions and sparse long-range connections, thereby effectively alleviating the over-globalizing issue. To further capture the brain’s modular architecture, we designe a prior-guided clustering module that utilizes a cross-attention mechanism to group brain regions into functional communities and leverage neuroanatomical prior to guide the clustering process, thereby improving the biological plausibility and interpretability. Experimental results indicate that our proposed method significantly improves performance of disease identification, and can reliably capture the sub-functional modules of the brain, demonstrating its interpretability.

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AAAI Conference 2026 Conference Paper

Dual-stream Relation-modeling Disentanglement for Cloth-Changing Person Re-Identification

Shijuan Huang
Hefei Ling
Zongyi Li
Xu Li
Zhao Lv

Cloth-changing person re-identification (CC-ReID) aims to identify individuals across non-overlapping cameras despite clothing variations. Existing methods are often constrained by two primary limitations: approaches using auxiliary modalities typically rely on a single specific cue, limiting their robustness, while feature disentanglement methods struggle with discrete labels that create inconsistencies between ground truth labels and modality semantic similarity. To overcome these limitations, we propose DRDnet, a unified framework that synergistically integrates dual auxiliary cues and advanced relation modeling. Specifically, our Dual-Stream Disentanglement (DSD) module leverages textual descriptions and parsing images to decouple clothing factors through high-level semantic supervision and pixel-level operations, yielding robust clothing-agnostic features. Simultaneously, our Modal Relation Modeling (MRM) module constructs feature memory banks and employs adaptive soft label smoothing, effectively enhancing image-text semantic alignment and reinforcing identity consistency across clothing changes. We evaluate DRDnet on several CC-ReID benchmarks to demonstrate its effectiveness and provide state-of-the-art performance across all benchmarks.

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AAAI Conference 2026 Conference Paper

Trainable EEG Interpolation and Structure-Sharing Dual-Path Encoders for Brain-Assisted Target Speaker Extraction

Zhao Lv
Haoran Zhou
Ying Chen
Youdian Gao
Xinhui Li
Ruibo Fu
Cunhang Fan

Brain-assisted target speaker extraction (TSE) isolates a target speaker's voice from a mixture by leveraging task-specific representations in Electroencephalogram (EEG) signals. However, existing methods rely on fixed interpolation for EEG-audio alignment, introducing redundant computations. They also employ single-path encoders that extract only target-relevant features while neglecting complementary, irrelevant ones, limiting discriminability. To address these limitations, this paper proposes a Trainable EEG Interpolation and Structure-sharing Dual-path Encoders network (TIDENet). The proposed Trainable EEG Interpolation (TEI) uses a neural network module to leverage cross-sample EEG information during resampling by parameters updating, thereby overcoming the limitations of fixed interpolation. The Structure-sharing Dual-path Encoders (SSDPE) extend existing speech and EEG encoders by introducing dual paths that separately process features relevant and irrelevant to the target speaker and incorporates interactive fusion between them, which enhances the encoder's ability to capture task-relevant information. Experimental results on public datasets demonstrate that TIDENet achieves relative improvements of up to 20.47%, 22.22%, 2.91%, 6.20%, and 15.84% in signal-to-distortion ratio (SDR), scale-invariant SDR (SI-SDR), short-time objective intelligibility (STOI), extended STOI (ESTOI), and perceptual evaluation of speech quality (PESQ), respectively, compared to the state-of-the-art. These significant gains validate the effectiveness of the proposed TEI method and SSDPE architecture.

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AAAI Conference 2025 Conference Paper

BSDB-Net: Band-Split Dual-Branch Network with Selective State Spaces Mechanism for Monaural Speech Enhancement

Cunhang Fan
Enrui Liu
Andong Li
Jianhua Tao
Jian Zhou
Jiahao Li
Chengshi Zheng
Zhao Lv

Although the complex spectrum-based speech enhancement (SE) methods have achieved significant performance, coupling amplitude and phase can lead to a compensation effect, where amplitude information is sacrificed to compensate for the phase that is harmful to SE. In addition, to further improve the performance of SE, many modules are stacked onto SE, resulting in increased model complexity that limits the application of SE. To address these problems, we proposed a dual-path network based on compressed frequency using Mamba. First, we extract amplitude and phase information through parallel dual branches. This approach leverages structured complex spectra to implicitly capture phase information and solves the compensation effect by decoupling amplitude and phase, and the network incorporates an interaction module to suppress unnecessary parts and recover missing components from the other branch. Second, to reduce network complexity, the network introduces a band-split strategy to compress the frequency dimension. To further reduce complexity while maintaining good performance, we designed a Mamba-based module that models the time and frequency dimensions under linear complexity. Finally, compared to baselines, our model achieves an average 8.3 times reduction in computational complexity while maintaining superior performance. Furthermore, it achieves a 25 times reduction in complexity compared to transformer-based models.

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IJCAI Conference 2025 Conference Paper

Community-Aware Graph Transformer for Brain Disorder Identification

Shengbing Pei
Jiajun Ma
Zhao Lv
Chao Zhang
Jihong Guan

Abnormal brain functional network is an effective biomarker for brain disease diagnosis. Most existing methods focus on mining discriminative information from whole-brain connectivity patterns. However, multi-level collaboration is the foundation of efficient brain function, in addition to the whole-brain network, there are multiple sub-networks that can quickly integrate and process specific cognitive functions, forming the modular community structure of the brain. To address this gap, we propose a novel method, community-aware graph Transformer (CAGT), that integrates the community information of sub-networks and the topological information of brain graph into the Transformer architecture for better brain disorder identification. CAGT enhances information exchange within and between functional communities through dual-scale feature fusion, capturing interactive information across various scales. Additionally, it incorporates prior knowledge to design brain region position encoding and guide the self-attention, thereby enhancing the spatial awareness of the Transformer and aligning it with the brain's natural information transfer process. Experimental results indicate that our proposed method significantly improves performance on both large and small datasets, and can reliably capture the interactions between sub-networks, demonstrating its generalization and interpretability.

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JBHI Journal 2025 Journal Article

CrossConvPyramid: Deep Multimodal Fusion for Epileptic Magnetoencephalography Spike Detection

Liang Zhang
Shurong Sheng
Xiongfei Wang
Jia-Hong Gao
Yi Sun
Kuntao Xiao
Wanli Yang
Pengfei Teng

Magnetoencephalography (MEG) is a vital non-invasive tool for epilepsy analysis, as it captures high-resolution signals that reflect changes in brain activity over time. The automated detection of epileptic spikes within these signals can significantly reduce the labor and time required for manual annotation of MEG recording data, thereby aiding clinicians in identifying epileptogenic foci and evaluating treatment prognosis. Research in this domain often utilizes the raw, multi-channel signals from MEG scans for spike detection, commonly neglecting the multi-channel spiking patterns from spatially adjacent channels. Moreover, epileptic spikes share considerable morphological similarities with artifact signals within the recordings, posing a challenge for models to differentiate between the two. In this paper, we introduce a multimodal fusion framework that addresses these two challenges collectively. Instead of relying solely on the signal recordings, our framework also mines knowledge from their corresponding topography-map images, which encapsulate the spatial context and amplitude distribution of the input signals. To facilitate more effective data fusion, we present a novel multimodal feature fusion technique called CrossConvPyramid, built upon a convolutional pyramid architecture augmented by an attention mechanism. It initially employs cross-attention and a convolutional pyramid to encode inter-modal correlations within the intermediate features extracted by individual unimodal networks. Subsequently, it utilizes a self-attention mechanism to refine and select the most salient features from both inter-modal and unimodal features, specifically tailored for the spike classification task. Our method achieved the average F1 scores of 92. 88% and 95. 23% across two distinct real-world MEG datasets from separate centers, respectively outperforming the current state-of-the-art by 2. 31% and 0. 88%. We plan to release the code on GitHub later.

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YNIMG Journal 2025 Journal Article

How do the resting EEG preprocessing states affect the outcomes of postprocessing?

Shiang Hu
Jie Ruan
Pedro Antonio Valdes-Sosa
Zhao Lv

Plenty of artifact removal tools and pipelines have been developed to correct the resting EEG waves and discover scientific values behind. Without expertised visual inspection, it is susceptible to derive improper preprocessing, resulting in either insufficient preprocessed EEG (IPE) or excessive preprocessed EEG (EPE). However, little is known about the impacts of IPE or EPE on postprocessing in the temporal, frequency, and spatial domains, particularly as to the spectra and the functional connectivity analysis. Here, the clean EEG (CE) with linear and quasi-stationary assumption was synthesized as ground truth based on the New-York head model and the multivariate autoregressive model. Later, IPE and EPE were simulated by injecting Gaussian noise and losing brain components, respectively. Spectral homogeneities of all EEGs were evaluated by the proposed Parallel LOg Spectra index (PaLOSi). Then, the impacts on postprocessing were quantified by the IPE/EPE deviation from CE as to the temporal statistics, multichannel power, cross spectra, scalp EEG network properties, and source dispersion. Lastly, the association between PaLOSi and varying trends of postprocessing outcomes was analyzed with evolutionary preprocessing states. We found that compared with CE: 1) IPE (EPE) temporal statistics deviated more greatly with more noise injected (brain activities discarded); 2) IPE (EPE) power was higher (lower), and IPE power was almost parallel to that of CE across frequencies, while EPE power deviation decreased with higher frequencies; IPE cross spectra deviated more greatly than EPE, except for β band; 3) derived from 7 coupling measures, IPE (EPE) network had lower (higher) transmission efficiency and worse (better) integration ability; 4) IPE sources distributed more dispersedly with greater strength while EPE sources activated more focally with lower amplitudes; 5) PaLOSi was consistently correlated with varying trends of investigated postprocessing for both simulated and real data. This study shed light on how the postprocessing outcomes are affected by the preprocessing states and PaLOSi is a promising quality control metric for creating normative EEG databases.

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IJCAI Conference 2025 Conference Paper

ID-RemovalNet: Identity Removal Network for EEG Privacy Protection with Enhancing Decoding Tasks

Huabin Wang
Jie Ruan
Cunhang Fan
Yingfan Cheng
Zhao Lv

Electroencephalogram (EEG) contains not only decoding task information but also personal identity privacy information. If it is stolen or attacked, the user's brain-computer interaction behavior may be maliciously manipulated. Existing EEG identity privacy protection generally adopts generative or adding tiny perturbation methods, which can protect the identity privacy in EEG signals to some extent. However, these methods also damage the performance of decoding task. In order to solve these problems, this paper proposes an identity removal network (ID-RemovalNet) to achieve EEG privacy protection while improving the classification accuracy of decoding task. Firstly, an identity decorrelation separation module is constructed to accurately remove the identity features to achieve privacy protection while reducing the interference with the task decoding features. Secondly, a multi-domain multi-level fusion feature extraction module is designed to extract the high-quality EEG time-frequency features. Finally, the feature enhancement module is used to compensate for the loss of task decoding features and excitation of dominant feature selection during identity feature removal. The experimental results show that ID-RemoveNet removes identity information to 0. 43% on four EEG datasets with two different paradigms, and significantly improves the EEG task decoding accuracy by 3. 28%, and achieves the state-of-the-art performance in cross-subject EEG experiment.

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IJCAI Conference 2025 Conference Paper

ListenNet: A Lightweight Spatio-Temporal Enhancement Nested Network for Auditory Attention Detection

Cunhang Fan
Xiaoke Yang
Hongyu Zhang
Ying Chen
Lu Li
Jian Zhou
Zhao Lv

Auditory attention detection (AAD) aims to identify the direction of the attended speaker in multi-speaker environments from brain signals, such as Electroencephalography (EEG) signals. However, existing EEG-based AAD methods overlook the spatio-temporal dependencies of EEG signals, limiting their decoding and generalization abilities. To address these issues, this paper proposes a Lightweight Spatio-Temporal Enhancement Nested Network (ListenNet) for AAD. The ListenNet has three key components: Spatio-temporal Dependency Encoder (STDE), Multi-scale Temporal Enhancement (MSTE), and Cross-Nested Attention (CNA). The STDE reconstructs dependencies between consecutive time windows across channels, improving the robustness of dynamic pattern extraction. The MSTE captures temporal features at multiple scales to represent both fine-grained and long-range temporal patterns. In addition, the CNA integrates hierarchical features more effectively through novel dynamic attention mechanisms to capture deep spatio-temporal correlations. Experimental results on three public datasets demonstrate the superiority of ListenNet over state-of-the-art methods in both subject-dependent and challenging subject-independent settings, while reducing the trainable parameter count by approximately 7 times. Code is available at: https: //github. com/fchest/ListenNet.

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IJCAI Conference 2025 Conference Paper

M3ANet: Multi-scale and Multi-Modal Alignment Network for Brain-Assisted Target Speaker Extraction

Cunhang Fan
Ying Chen
Jian Zhou
Zexu Pan
Jingjing Zhang
Youdian Gao
Xiaoke Yang
Zhengqi Wen

The brain-assisted target speaker extraction (TSE) aims to extract the attended speech from mixed speech by utilizing the brain neural activities, for example Electroencephalography (EEG). However, existing models overlook the issue of temporal misalignment between speech and EEG modalities, which hampers TSE performance. In addition, the speech encoder in current models typically uses basic temporal operations (e. g. , one-dimensional convolution), which are unable to effectively extract target speaker information. To address these issues, this paper proposes a multi-scale and multi-modal alignment network (M3ANet) for brain-assisted TSE. Specifically, to eliminate the temporal inconsistency between EEG and speech modalities, the modal alignment module that uses a contrastive learning strategy is applied to align the temporal features of both modalities. Additionally, to fully extract speech information, multi-scale convolutions with GroupMamba modules are used as the speech encoder, which scans speech features at each scale from different directions, enabling the model to capture deep sequence information. Experimental results on three publicly available datasets show that the proposed model outperforms current state-of-the-art methods across various evaluation metrics, highlighting the effectiveness of our proposed method. The source code is available at: https: //github. com/fchest/M3ANet.

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IJCAI Conference 2025 Conference Paper

MHANet: Multi-scale Hybrid Attention Network for Auditory Attention Detection

Lu Li
Cunhang Fan
Hongyu Zhang
Jingjing Zhang
Xiaoke Yang
Jian Zhou
Zhao Lv

Auditory attention detection (AAD) aims to detect the target speaker in a multi-talker environment from brain signals, such as electroencephalography (EEG), which has made great progress. However, most AAD methods solely utilize attention mechanisms sequentially and overlook valuable multi-scale contextual information within EEG signals, limiting their ability to capture long-short range spatiotemporal dependencies simultaneously. To address these issues, this paper proposes a multi-scale hybrid attention network (MHANet) for AAD, which consists of the multi-scale hybrid attention (MHA) module and the spatiotemporal convolution (STC) module. Specifically, MHA combines channel attention and multi-scale temporal and global attention mechanisms. This effectively extracts multi-scale temporal patterns within EEG signals and captures long-short range spatiotemporal dependencies simultaneously. To further improve the performance of AAD, STC utilizes temporal and spatial convolutions to aggregate expressive spatiotemporal representations. Experimental results show that the proposed MHANet achieves state-of-the-art performance with fewer trainable parameters across three datasets, 3 times lower than that of the most advanced model. Code is available at: https: //github. com/fchest/MHANet.

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AAAI Conference 2025 Conference Paper

Region-Based Optimization in Continual Learning for Audio Deepfake Detection

Yujie Chen
Jiangyan Yi
Cunhang Fan
Jianhua Tao
Yong Ren
Siding Zeng
Chu Yuan Zhang
Xinrui Yan

Rapid advancements in speech synthesis and voice conversion bring convenience but also new security risks, creating an urgent need for effective audio deepfake detection. Although current models perform well, their effectiveness diminishes when confronted with the diverse and evolving nature of real-world deepfakes. To address this issue, we propose a continual learning method named Region-Based Optimization (RegO) for audio deepfake detection. Specifically, we use the Fisher information matrix to measure important neuron regions for real and fake audio detection, dividing them into four regions. First, we directly fine-tune the less important regions to quickly adapt to new tasks. Next, we apply gradient optimization in parallel for regions important only to real audio detection, and in orthogonal directions for regions important only to fake audio detection. For regions that are important to both, we use sample proportion-based adaptive gradient optimization. This region-adaptive optimization ensures an appropriate trade-off between memory stability and learning plasticity. Additionally, to address the increase of redundant neurons from old tasks, we further introduce the Ebbinghaus forgetting mechanism to release them, thereby promoting the model’s ability to learn more generalized discriminative features. Experimental results show our method achieves a 21.3 percent improvement in EER over the state-of-the-art continual learning approach RWM for audio deepfake detection. Moreover, the effectiveness of RegO extends beyond the audio deepfake detection domain, showing potential significance in other tasks, such as image recognition.

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AAAI Conference 2024 Conference Paper

A Non-parametric Graph Clustering Framework for Multi-View Data

Shengju Yu
Siwei Wang
Zhibin Dong
Wenxuan Tu
Suyuan Liu
Zhao Lv
Pan Li
Miao Wang

Multi-view graph clustering (MVGC) derives encouraging grouping results by seamlessly integrating abundant information inside heterogeneous data, and has captured surging focus recently. Nevertheless, the majority of current MVGC works involve at least one hyper-parameter, which not only requires additional efforts for tuning, but also leads to a complicated solving procedure, largely harming the flexibility and scalability of corresponding algorithms. To this end, in the article we are devoted to getting rid of hyper-parameters, and devise a non-parametric graph clustering (NpGC) framework to more practically partition multi-view data. To be specific, we hold that hyper-parameters play a role in balancing error item and regularization item so as to form high-quality clustering representations. Therefore, under without the assistance of hyper-parameters, how to acquire high-quality representations becomes the key. Inspired by this, we adopt two types of anchors, view-related and view-unrelated, to concurrently mine exclusive characteristics and common characteristics among views. Then, all anchors' information is gathered together via a consensus bipartite graph. By such ways, NpGC extracts both complementary and consistent multi-view features, thereby obtaining superior clustering results. Also, linear complexities enable it to handle datasets with over 120000 samples. Numerous experiments reveal NpGC's strong points compared to lots of classical approaches.

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NeurIPS Conference 2024 Conference Paper

DARNet: Dual Attention Refinement Network with Spatiotemporal Construction for Auditory Attention Detection

Sheng Yan
Cunhang Fan
Hongyu Zhang
Xiaoke Yang
Jianhua Tao
Zhao Lv

At a cocktail party, humans exhibit an impressive ability to direct their attention. The auditory attention detection (AAD) approach seeks to identify the attended speaker by analyzing brain signals, such as EEG signals. However, current AAD algorithms overlook the spatial distribution information within EEG signals and lack the ability to capture long-range latent dependencies, limiting the model's ability to decode brain activity. To address these issues, this paper proposes a dual attention refinement network with spatiotemporal construction for AAD, named DARNet, which consists of the spatiotemporal construction module, dual attention refinement module, and feature fusion & classifier module. Specifically, the spatiotemporal construction module aims to construct more expressive spatiotemporal feature representations, by capturing the spatial distribution characteristics of EEG signals. The dual attention refinement module aims to extract different levels of temporal patterns in EEG signals and enhance the model's ability to capture long-range latent dependencies. The feature fusion & classifier module aims to aggregate temporal patterns and dependencies from different levels and obtain the final classification results. The experimental results indicate that DARNet achieved excellent classification performance, particularly under short decision windows. While maintaining excellent classification performance, DARNet significantly reduces the number of required parameters. Compared to the state-of-the-art models, DARNet reduces the parameter count by 91\%. Code is available at: https: //github. com/fchest/DARNet. git.

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IJCAI Conference 2024 Conference Paper

DBPNet: Dual-Branch Parallel Network with Temporal-Frequency Fusion for Auditory Attention Detection

Qinke Ni
Hongyu Zhang
Cunhang Fan
Shengbing Pei
Chang Zhou
Zhao Lv

Auditory attention decoding (AAD) aims to recognize the attended speaker based on electroencephalography (EEG) signals in multi-talker environments. Most AAD methods only focus on the temporal or frequency domain, but neglect the relationships between these two domains, which results in the inability to simultaneously consider both time-varying and spectral-spatial information. To address this issue, this paper proposes a dual-branch parallel network with temporal-frequency fusion for AAD, named DBPNet, which consists of the temporal attentive branch and the frequency residual branch. Specifically, the temporal attentive branch aims to capture the time-varying features in the EEG time-series signal. The frequency residual branch aims to extract spectral-spatial features of multi-band EEG signals by the residual convolution. Finally, these dual branches are fused to consider both EEG signals time-varying and spectral-spatial features and get classification results. Experimental results show that compared with the best baseline, DBPNet achieves a relative improvement of 20. 4% with a 0. 1-second decision window for the MM-AAD dataset, but the number of trainable parameters is reduced by about 91 times.

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AAAI Conference 2024 Conference Paper

Progressive Distillation Based on Masked Generation Feature Method for Knowledge Graph Completion

Cunhang Fan
Yujie Chen
Jun Xue
Yonghui Kong
Jianhua Tao
Zhao Lv

In recent years, knowledge graph completion (KGC) models based on pre-trained language model (PLM) have shown promising results. However, the large number of parameters and high computational cost of PLM models pose challenges for their application in downstream tasks. This paper proposes a progressive distillation method based on masked generation features for KGC task, aiming to significantly reduce the complexity of pre-trained models. Specifically, we perform pre-distillation on PLM to obtain high-quality teacher models, and compress the PLM network to obtain multi-grade student models. However, traditional feature distillation suffers from the limitation of having a single representation of information in teacher models. To solve this problem, we propose masked generation of teacher-student features, which contain richer representation information. Furthermore, there is a significant gap in representation ability between teacher and student. Therefore, we design a progressive distillation method to distill student models at each grade level, enabling efficient knowledge transfer from teachers to students. The experimental results demonstrate that the model in the pre-distillation stage surpasses the existing state-of-the-art methods. Furthermore, in the progressive distillation stage, the model significantly reduces the model parameters while maintaining a certain level of performance. Specifically, the model parameters of the lower-grade student model are reduced by 56.7\% compared to the baseline.

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JBHI Journal 2020 Journal Article

To Explore the Potentials of Independent Component Analysis in Brain-Computer Interface of Motor Imagery

Xiaopei Wu
Bangyan Zhou
Zhao Lv
Chao Zhang

This paper is focused on the experimental approach to explore the potential of independent component analysis (ICA) in the context of motor imagery (MI)-based brain-computer interface (BCI). We presented a simple and efficient algorithmic framework of ICA-based MI BCI (ICA-MIBCI) for the evaluation of four classical ICA algorithms (Infomax, FastICA, Jade, and Sobi) as well as a simplified Infomax (sInfomax). Two novel performance indexes, self-test accuracy and the number of invalid ICA filters, were employed to assess the performance of MIBCI based on different ICA variants. As a reference method, common spatial pattern (CSP), a commonly-used spatial filtering method, was employed for the comparative study between ICA-MIBCI and CSP-MIBCI. The experimental results showed that sInfomax-based spatial filters exhibited significantly better transferability in session to session and subject to subject transfer as compared to CSP-based spatial filters. The online experiment was also introduced to demonstrate the practicability and feasibility of sInfomax-based MIBCI. However, four classical ICA variants, especially FastICA, Jade, and Sobi, performed much worse as compared to sInfomax and CSP in terms of classification accuracy and stability. We consider that conventional ICA-based spatial filtering methods tend to be overfitting while applied to real-life electroencephalogram data. Nevertheless, the sInfomax-based experimental results indicate that ICA methods have a great space for improvement in the application of MIBCI. We believe that this paper could bring forth new ideas for the practical implementation of ICA-MIBCI.

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