Author name cluster

Ziyu Jia

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

20 papers

2 author rows

AAAI Conference 2026 Conference Paper

DarkFarseer: Robust Spatio-Temporal Kriging Under Graph Sparsity and Noise

Zhuoxuan Liang
Wei Li
Dalin Zhang
Ziyu Jia
Yidan Chen
Zhihong Wang
Xiangping Zheng
Moustafa Youssef

The rapid expansion of the Internet of Things (IoT) has created a growing demand for large-scale sensor deployment. However, the high cost of physical sensors limits the scalability and coverage of sensor networks, making fine-grained sensing difficult. Inductive Spatio-Temporal Kriging (ISK) addresses this challenge by introducing virtual sensors that infer measurements from physical sensors, typically using graph neural networks (GNNs) to model their relationships. Despite its promise, current ISK methods often rely on standard message-passing and generic architectures that fail to effectively capture spatio-temporal features or represent virtual nodes accurately. Additionally, existing graph construction techniques suffer from sparse and noisy connections, further hindering performance. To address these limitations, we propose DarkFarseer, a novel ISK framework with three key innovations. First, the Style-enhanced Temporal-Spatial architecture adopts a temporal-then-spatial processing scheme with a temporal style transfer mechanism to enhance virtual node representations. Second, Regional-semantic Contrastive Learning improves representation learning by aligning virtual nodes with regional component patterns. Third, the Similarity-Based Graph Denoising Strategy mitigates the influence of noisy edges by leveraging temporal similarity and regional structure. Extensive experiments on real-world datasets demonstrate that DarkFarseer significantly outperforms state-of-the-art ISK methods.

PDF Details DOI

JBHI Journal 2026 Journal Article

Dual-Branch Attention-Based Frequency Domain Network for Cross-Subject SSVEP-BCIs

Yi Yang
Ze Wang
Ziyu Jia
Boyu Wang
Shangen Zhang
Chi Man Wong
Xiaorong Gao
Tzyy-Ping Jung

Steady-state visual evoked potential-based brain-computer interfaces (SSVEP-BCIs) hold significant promise for enabling high-speed human-computer interaction in real-world scenarios. However, existing frequency-domain decoding methods treat frequency spectrum features (the real and imaginary spectrum features) as a single feature without considering their unique spatial and spectral characteristics, resulting in insufficient generalizable features and limited classification accuracy in cross-subject scenarios. To address this issue, we propose a Dual-Branch Attention-Based Frequency Domain Network (DB-AFDNet) to independently decode real and imaginary spectral components, aiming to acquire more discriminative and generalizable features for cross-subject applications. Specifically, we construct inter-branch attention similarity constraints to encourage the two branches to have similar attention properties, promoting to learn the consensus characteristics in the dual branches. Furthermore, we propose intra-branch orthogonality constraints to explore branch-specific discriminative features to learn generalizable features. Experimental studies on two public datasets, the Benchmark and Beta datasets, demonstrate that DB-AFDNet outperforms state-of-the-art methods in cross-subject classification, achieving a relative improvement of 1. 36 $\%$ and 1. 45 $\%$, respectively.

Details DOI

JBHI Journal 2026 Journal Article

MSDB-Mam: Dual-Branch Mamba Network With Multi-Scale Features for EEG-Based Depression Detection

Cheng Cheng
Wenzhe Liu
Peiyang Li
Ziyu Jia
Wenbo Luo

Depression is a widespread mental illness, and EEG-based detection offers a non-invasive means to reflect brain activity. However, the inherent complexity of EEG signals across spatial, temporal, and spectral domains poses major challenges for accurate detection, since brain activities vary dynamically over time, differ across brain regions, and are distributed across multiple frequency bands. To this end, we propose a novel Multi-Scale Dual-Branch Mamba network (MSDB-Mam) that efficiently extracts and fuses multi-dimensional EEG features. Specifically, a Multi-Scale Convolution (MSC) module captures diverse patterns along temporal and spatial-temporal axes using different kernel sizes. An Adaptive Reallocation (AR) unit dynamically adjusts feature weights to highlight informative patterns. To capture long-range and cross-domain dependencies, we introduce a Dual-Branch Mamba (DB-Mam) architecture, consisting of a Temporal–Spectral Mamba (TS-Mam) branch for modeling temporal–frequency correlations and a Spatial–Temporal–Spectral Mamba (STS-Mam) branch for learning richer interactions across spatial, temporal, and frequency. The features from both branches are subsequently fused to form a comprehensive and expressive EEG representation. Experiments on MODMA and PRED+CT datasets show that our method achieves 96. 58% and 96. 66% accuracy, respectively, surpassing existing approaches and demonstrating its effectiveness in EEG-based depression detection.

Details DOI

IJCAI Conference 2025 Conference Paper

A Cross-Modal Densely Guided Knowledge Distillation Based on Modality Rebalancing Strategy for Enhanced Unimodal Emotion Recognition

Shuang Wu
Heng Liang
Yong Zhang
Yanlin Chen
Ziyu Jia

Multimodal emotion recognition has garnered significant attention for its ability to integrate data from multiple modalities to enhance performance. However, physiological signals like electroencephalogram are more challenging to acquire than visual data due to higher collection costs and complexity. This limits the practical application of multimodal networks. To address this issue, this paper proposes a cross-modal knowledge distillation framework for emotion recognition. The framework aims to leverage the strengths of a multimodal teacher network to enhance the performance of a unimodal student network using only the visual modality as input. Specifically, we design a prototype-based modality rebalancing strategy, which dynamically adjusts the convergence rates of different modalities to mitigate modality imbalance issue. It enables the teacher network to better integrate multimodal information. Building upon this, we develop a Cross-Modal Densely Guided Knowledge Distillation (CDGKD) method, which effectively transfers knowledge extracted by the multimodal teacher network to the unimodal student network. Our CDGKD uses multi-level teacher assistant networks to bridge the teacher-student gap and employs dense guidance to reduce error accumulation during knowledge transfer. Experimental results demonstrate that the proposed framework outperforms existing methods on two public emotion datasets, providing an effective solution for emotion recognition in modality-constrained scenarios.

PDF Details DOI

NeurIPS Conference 2025 Conference Paper

A Multimodal BiMamba Network with Test-Time Adaptation for Emotion Recognition Based on Physiological Signals

Ziyu Jia
Tingyu Du
Zhengyu Tian
Hongkai Li
Yong Zhang
Chenyu Liu

Emotion recognition based on physiological signals plays a vital role in psychological health and human–computer interaction, particularly with the substantial advances in multimodal emotion recognition techniques. However, two key challenges remain unresolved: 1) how to effectively model the intra-modal long-range dependencies and inter-modal correlations in multimodal physiological emotion signals, and 2) how to address the performance limitations resulting from missing multimodal data. In this paper, we propose a multimodal bidirectional Mamba (BiMamba) network with test-time adaptation (TTA) for emotion recognition named BiM-TTA. Specifically, BiM-TTA consists of a multimodal BiMamba network and a multimodal TTA. The former includes intra-modal and inter-modal BiMamba modules, which model long-range dependencies along the time dimension and capture cross-modal correlations along the channel dimension, respectively. The latter (TTA) mitigates the amplified distribution shifts caused by missing multimodal data through two-level entropy-based sample filtering and mutual information sharing across modalities. By addressing these challenges, BiM-TTA achieves state-of-the-art results on two multimodal emotion datasets.

PDF Details

JBHI Journal 2025 Journal Article

DynSeizureGAT: Multi-Band Dynamic Graph Attention Network for Interpretable Seizure Detection and Analysis of Drug-Resistant Epilepsy Using SEEG

Yiping Wang
Jinjie Guo
Ziyu Jia
Gongpeng Cao
Yanfeng Yang
Guixia Kang
Jinguo Huang

The dynamic propagation of epileptic discharges complicates Drug-Resistant Epilepsy (DRE) seizure detection using traditional machine learning methods and Stereotactic Electroencephalography (SEEG). Several challenges remain unresolved in prior studies: (1) incomprehensive representations of epileptic brain network features; (2) lacking of flexible and dynamic mechanisms to learn brain network evolving features; and (3) the absence of model mechanisms interpretation corresponds with seizure mechanisms. In response, we propose a novel multi-band dynamic graph attention network, DynSeizureGAT, to detect and analyze DRE seizures with precision and interpretability. Specifically, a seizure network sequence is first constructed by integrating a multi-band directed transfer function matrix and enhanced epileptic index node features. Second, a dynamic graph attention module is integrated to dynamically weigh the contribution of various spatial scales. Third, spatial-spectral-temporal attention mechanisms enhance the model’s capacity to better characterize and interpret the ictal and interictal states. Extensive experiments are conducted on the large-scale public clinical SEEG dataset (OpenNeuro). The proposed model demonstrates high seizure detection performance, achieving an average of 94. 6% accuracy, 93. 4% sensitivity, and 96. 4% specificity. In addition, the importance of frequency bands and dynamic abnormal connectivity patterns is successfully quantified and visualized, which contributes most to the explainability. Experimental results indicate that DynSeizureGAT demonstrates strong dynamic propagation feature learning capability, corresponding with seizure propagation mechanisms, and is promising to assist DRE epileptogenic zone localization.

Details DOI

NeurIPS Conference 2025 Conference Paper

REFED: A Subject Real-time Dynamic Labeled EEG-fNIRS Synchronized Recorded Emotion Dataset

Xiaojun Ning
Jing Wang
Zhiyang Feng
Tianzuo Xin
Shuo Zhang
Shaoqi Zhang
Zheng Lian
Yi Ding

Affective brain-computer interfaces (aBCIs) play a crucial role in personalized human–computer interaction and neurofeedback modulation. To develop practical and effective aBCI paradigms and to investigate the spatial-temporal dynamics of brain activity under emotional inducement, portable electroencephalography (EEG) signals have been widely adopted. To further enhance spatial-temporal perception, functional near-infrared spectroscopy (fNIRS) has attracted increasing interest in the aBCI field and has been explored in combination with EEG. However, existing datasets typically provide only static fixation labels, overlooking the dynamic changes in subjects' emotions. Notably, some studies have attempted to collect continuously annotated emotional data, but they have recorded only peripheral physiological signals without directly observing brain activity, limiting insight into underlying neural states under different emotions. To address these challenges, we present the Real-time labeled EEG-fNIRS Dataset (REFED). To the best of our knowledge, this is the first EEG-fNIRS dataset with real-time dynamic emotional annotations. REFED simultaneously records brain signals from both EEG and fNIRS modalities while providing continuous, real-time annotations of valence and arousal. The results of the data analysis demonstrate the effectiveness of emotion inducement and the reliability of real-time annotation. This dataset offers the possibility for studying the neurovascular coupling mechanism under emotional evolution and for developing dynamic, robust affective BCIs.

PDF Details

ICLR Conference 2025 Conference Paper

SleepSMC: Ubiquitous Sleep Staging via Supervised Multimodal Coordination

Shuo Ma 0001
Yingwei Zhang 0002
Yiqiang Chen 0001
Hualei Wang
Yuan Jin
Wei Zhang 0082
Ziyu Jia

Sleep staging is critical for assessing sleep quality and tracking health. Polysomnography (PSG) provides comprehensive multimodal sleep-related information, but its complexity and impracticality limit its practical use in daily and ubiquitous monitoring. Conversely, unimodal devices offer more convenience but less accuracy. Existing multimodal learning paradigms typically assume that the data types remain consistent between the training and testing phases. This makes it challenging to leverage information from other modalities in ubiquitous scenarios (e.g., at home) where only one modality is available. To address this issue, we introduce a novel framework for ubiquitous Sleep staging via Supervised Multimodal Coordination, called SleepSMC. To capture category-related consistency and complementarity across modality-level instances, we propose supervised modality-level instance contrastive coordination. Specifically, modality-level instances within the same category are considered positive pairs, while those from different categories are considered negative pairs. To explore the varying reliability of auxiliary modalities, we calculate uncertainty estimates based on the variance in confidence scores for correct predictions during multiple rounds of random masks. These uncertainty estimates are employed to assign adaptive weights to multiple auxiliary modalities during contrastive learning, ensuring that the primary modality learns from high-quality, category-related features. Experimental results on four public datasets, ISRUC-S3, MASS-SS3, Sleep-EDF-78, and ISRUC-S1, show that SleepSMC achieves state-of-the-art cross-subject performance. SleepSMC significantly improves performance when only one modality is present during testing, making it suitable for ubiquitous sleep monitoring.

Details

IJCAI Conference 2025 Conference Paper

ST-USleepNet: A Spatial-Temporal Coupling Prominence Network for Multi-Channel Sleep Staging

Jingying Ma
Qika Lin
Ziyu Jia
Mengling Feng

Sleep staging is critical to assess sleep quality and diagnose disorders. Despite advancements in artificial intelligence enabling automated sleep staging, significant challenges remain: (1) Simultaneously extracting prominent temporal and spatial sleep features from multi-channel raw signals, including characteristic sleep waveforms and salient spatial brain networks. (2) Capturing the spatial-temporal coupling patterns essential for accurate sleep staging. To address these challenges, we propose a novel framework named ST-USleepNet, comprising a spatial-temporal graph construction module (ST) and a U-shaped sleep network (USleepNet). The ST module converts raw signals into a spatial-temporal graph based on signal similarity, temporal, and spatial relationships to model spatial-temporal coupling patterns. The USleepNet employs a U-shaped structure for both the temporal and spatial streams, mirroring its original use in image segmentation to isolate significant targets. Applied to raw sleep signals and graph data from the ST module, USleepNet effectively segments these inputs, simultaneously extracting prominent temporal and spatial sleep features. Testing on three datasets demonstrates that ST-USleepNet outperforms existing baselines, and model visualizations confirm its efficacy in extracting prominent sleep features and temporal-spatial coupling patterns across various sleep stages. The code is available at https: //github. com/Majy-Yuji/ST-USleepNet.

PDF Details DOI

JBHI Journal 2025 Journal Article

Subject-Adaptation Salient Wave Detection Network for Multimodal Sleep Stage Classification

Jing Wang
Xuehui Wang
Xiaojun Ning
Youfang Lin
Huy Phan
Ziyu Jia

Sleep stage classification is an important step in the diagnosis and treatment of sleep disorders. Despite the high classification performance of previous sleep stage classification work, some challenges remain unresolved: 1) How to effectively capture salient waves in sleep signals to improve sleep stage classification results. 2) How to capture salient waves affected by inter-subject variability. 3) How to adaptively regulate the importance of different modals for different sleep stages. To address these challenges, we propose SleepWaveNet, a multimodal salient wave detection network, which is motivated by the salient object detection task in computer vision. It has a U-Transformer structure to detect salient waves in sleep signals. Meanwhile, the subject-adaptation wave extraction architecture based on transfer learning can adapt to the information of target individuals and extract salient waves with inter-subject variability. In addition, the multimodal attention module can adaptively enhance the importance of specific modal data for sleep stage classification tasks. Experiments on three datasets show that SleepWaveNet has better overall performance than existing baselines. Moreover, visualization experiments show that the model has the ability to capture salient waves with inter-subject variability.

Details DOI

IJCAI Conference 2024 Conference Paper

ATTA: Adaptive Test-Time Adaptation for Multi-Modal Sleep Stage Classification

Ziyu Jia
Xihao Yang
Chenyang Zhou
Haoyang Deng
Tianzi Jiang

Sleep stage classification is crucial for sleep quality assessment and disease diagnosis. Although some recent studies have made great strides in sleep stage classification performance, direct application to multi-modal sleep data with cross-domain distributional variations still poses challenges: 1) How to retain the sleep knowledge acquired by the model from the source domain during cross-domain adaptation to avoid catastrophic forgetting. 2) How to evaluate the contribution of different modalities in identifying specific sleep stages to serve test-time adaptation (TTA). 3) How to dynamically adapt the sleep model to different distribution shift in data domains of different subjects. To address these challenges, we propose an Adaptive Test-Time Adaptation (ATTA) method, a multi-modal test-time adaptation method for sleep stage classification. Specifically, the intra-modal retained-adaptive module is proposed for adapting to the target domain data while retaining the sleep knowledge acquired from the source domain to avoid catastrophic forgetting. The inter-modal contribution assessment module is designed to adaptively assess the contribution of each modality to the identification of specific sleep stages. Furthermore, the adaptive learning rate strategy utilizes a memory bank to record data from different subjects during testing, and based on this, it measures the differences between the target subject and those in the memory bank. According to the difference, the model adapts to the subject samples with different learning rates. We conduct experiments on mutual migration on two sleep datasets, SleepEDF and SHHS. The results show that our ATTA method outperforms state-of-the-art baselines in sleep stage classification.

PDF Details DOI

TIST Journal 2024 Journal Article

Exploring Structure Incentive Domain Adversarial Learning for Generalizable Sleep Stage Classification

Shuo Ma
Yingwei Zhang
Yiqiang Chen
Tao Xie
Shuchao Song
Ziyu Jia

Sleep stage classification is crucial for sleep state monitoring and health interventions. In accordance with the standards prescribed by the American Academy of Sleep Medicine, a sleep episode follows a specific structure comprising five distinctive sleep stages that collectively form a sleep cycle. Typically, this cycle repeats about five times, providing an insightful portrayal of the subject’s physiological attributes. The progress of deep learning and advanced domain generalization methods allows automatic and even adaptive sleep stage classification. However, applying models trained with visible subject data to invisible subject data remains challenging due to significant individual differences among subjects. Motivated by the periodic category-complete structure of sleep stage classification, we propose a Structure Incentive Domain Adversarial learning (SIDA) method that combines the sleep stage classification method with domain generalization to enable cross-subject sleep stage classification. SIDA includes individual domain discriminators for each sleep stage category to decouple subject dependence differences among different categories and fine-grained learning of domain-invariant features. Furthermore, SIDA directly connects the label classifier and domain discriminators to promote the training process. Experiments on three benchmark sleep stage classification datasets demonstrate that the proposed SIDA method outperforms other state-of-the-art sleep stage classification and domain generalization methods and achieves the best cross-subject sleep stage classification results.

Details DOI

IJCAI Conference 2024 Conference Paper

Multi-level Disentangling Network for Cross-Subject Emotion Recognition Based on Multimodal Physiological Signals

Ziyu Jia
Fengming Zhao
Yuzhe Guo
Hairong Chen
Tianzi Jiang

Emotion recognition based on multimodal physiological signals is attracting more and more attention. However, how to deal with the consistency and heterogeneity of multimodal physiological signals, as well as individual differences across subjects, pose two significant challenges. In this paper, we propose a Multi-level Disentangling Network named MDNet for cross-subject emotion recognition based on multimodal physiological signals. Specifically, MDNet consists of a modality-level disentangling module and a subject-level disentangling module. The modality-level disentangling module projects multimodal physiological signals into modality-invariant subspace and modality-specific subspace, capturing modality-invariant features and modality-specific features. The subject-level disentangling module separates subject-shared features and subject-private features among different subjects from multimodal data, which facilitates cross-subject emotion recognition. Experiments on two multimodal emotion datasets demonstrate that MDNet outperforms other state-of-the-art baselines.

PDF Details DOI

IJCAI Conference 2024 Conference Paper

SDformer: Transformer with Spectral Filter and Dynamic Attention for Multivariate Time Series Long-term Forecasting

Ziyu Zhou
Gengyu Lyu
Yiming Huang
Zihao Wang
Ziyu Jia
Zhen Yang

Transformer has gained widespread adoption in modeling time series due to the exceptional ability of its self-attention mechanism in capturing long-range dependencies. However, when processing time series data with numerous variates, the vanilla self-attention mechanism tends to distribute attention weights evenly and smoothly, causing row-homogenization in attention maps and further hampering time series forecasting. To tackle this issue, we propose an advanced Transformer architecture entitled SDformer, which designs two novel modules, Spectral-Filter-Transform (SFT) and Dynamic-Directional-Attention (DDA), and integrates them into the encoder of Transformer to achieve more intensive attention allocation. Specifically, the SFT module utilizes the Fast Fourier Transform to select the most prominent frequencies, along with a Hamming Window to smooth and denoise the filtered series data; The DDA module applies a specialized kernel function to the query and key vectors projected from the denoised data, concentrating this innovative attention mechanism more effectively on the most informative variates to obtain a sharper attention distribution. These two modules jointly enable attention weights to be more salient among numerous variates, which in turn enhances the attention's ability to capture multivariate correlations, improving the performance in forecasting. Extensive experiments on public datasets demonstrate its superior performance over other state-of-the-art models. Code is available at https: //github. com/zhouziyu02/SDformer.

PDF Details DOI

ICLR Conference 2024 Conference Paper

VBH-GNN: Variational Bayesian Heterogeneous Graph Neural Networks for Cross-subject Emotion Recognition

Chenyu Liu
Xinliang Zhou
Zhengri Zhu
Liming Zhai
Ziyu Jia
Yang Liu 0003

The research on human emotion under electroencephalogram (EEG) is an emerging field in which cross-subject emotion recognition (ER) is a promising but challenging task. Many approaches attempt to find emotionally relevant domain-invariant features using domain adaptation (DA) to improve the accuracy of cross-subject ER. However, two problems still exist with these methods. First, only single-modal data (EEG) is utilized, ignoring the complementarity between multi-modal physiological signals. Second, these methods aim to completely match the signal features between different domains, which is difficult due to the extreme individual differences of EEG. To solve these problems, we introduce the complementarity of multi-modal physiological signals and propose a new method for cross-subject ER that does not align the distribution of signal features but rather the distribution of spatio-temporal relationships between features. We design a Variational Bayesian Heterogeneous Graph Neural Network (VBH-GNN) with Relationship Distribution Adaptation (RDA). The RDA first aligns the domains by expressing the model space as a posterior distribution of a heterogeneous graph for a given source domain. Then, the RDA transforms the heterogeneous graph into an emotion-specific graph to further align the domains for the downstream ER task. Extensive experiments on two public datasets, DEAP and Dreamer, show that our VBH-GNN outperforms state-of-the-art methods in cross-subject scenarios.

Details

IJCAI Conference 2024 Conference Paper

VSGT: Variational Spatial and Gaussian Temporal Graph Models for EEG-based Emotion Recognition

Chenyu Liu
Xinliang Zhou
Jiaping Xiao
Zhengri Zhu
Liming Zhai
Ziyu Jia
Yang Liu

Electroencephalogram (EEG), which directly reflects the emotional activity of the brain, has been increasingly utilized for emotion recognition. Most works exploit the spatial and temporal dependencies in EEG to learn emotional feature representations, but they still have two limitations to reach their full potential. First, prior knowledge is rarely used to capture the spatial dependency of brain regions. Second, the cross temporal dependency between consecutive time slices for different brain regions is ignored. To address these limitations, in this paper, we propose Variational Spatial and Gaussian Temporal (VSGT) graph models to investigate the spatial and temporal dependencies for EEG-based emotion recognition. The VSGT has two key components: Variational Spatial Encoder (VSE) and Gaussian Temporal Encoder (GTE). The VSE leverages the upper bound theorem to identify the dynamic spatial dependency based on prior knowledge by the variational Bayesian method. Besides, the GTE exploits the conditional Gaussian graph transform that computes comprehensive temporal dependency between consecutive time slices. Finally, the VSGT utilizes a recurrent structure to calculate the spatial and temporal dependencies for all time slices. Extensive experiments show the superiority of VSGT over state-of-the-art methods on multiple EEG datasets.

PDF Details DOI

ICLR Conference 2023 Conference Paper

BSTT: A Bayesian Spatial-Temporal Transformer for Sleep Staging

Yuchen Liu
Ziyu Jia

Sleep staging is helpful in assessing sleep quality and diagnosing sleep disorders. However, how to adequately capture the temporal and spatial relations of the brain during sleep remains a challenge. In particular, existing methods cannot adaptively infer spatial-temporal relations of the brain under different sleep stages. In this paper, we propose a novel Bayesian spatial-temporal relation inference neural network, named Bayesian spatial-temporal transformer (BSTT), for sleep staging. Our model is able to adaptively infer brain spatial-temporal relations during sleep for spatial-temporal feature modeling through a well-designed Bayesian relation inference component. Meanwhile, our model also includes a spatial transformer for extracting brain spatial features and a temporal transformer for capturing temporal features. Experiments show that our BSTT outperforms state-of-the-art baselines on ISRUC and MASS datasets. In addition, the visual analysis shows that the spatial-temporal relations obtained by BSTT inference have certain interpretability for sleep staging.

Details

IJCAI Conference 2023 Conference Paper

Teacher Assistant-Based Knowledge Distillation Extracting Multi-level Features on Single Channel Sleep EEG

Heng Liang
Yucheng Liu
Haichao Wang
Ziyu Jia

Sleep stage classification is of great significance to the diagnosis of sleep disorders. However, existing sleep stage classification models based on deep learning are usually relatively large in size (wider and deeper), which makes them hard to be deployed on wearable devices. Therefore, it is a challenge to lighten the existing sleep stage classification models. In this paper, we propose a novel general knowledge distillation framework for sleep stage classification tasks called SleepKD. Our SleepKD, composed of the multi-level module, teacher assistant module, and other knowledge distillation modules, aims to lighten large-scale sleep stage classification models. Specifically, the multi-level module is able to transfer the multi-level knowledge extracted from sleep signals by the teacher model (large-scale model) to the student model (lightweight model). Moreover, the teacher assistant module bridges the large gap between the teacher and student network, and further improves the distillation. We evaluate our method on two public sleep datasets (Sleep-EDF and ISRUC-III). Compared to the baseline methods, the results show that our knowledge distillation framework achieves state-of-the-art performance. SleepKD can significantly lighten the sleep model while maintaining its classification performance. The source code is available at https: //github. com/HychaoWang/SleepKD.

PDF Details DOI

IJCAI Conference 2021 Conference Paper

SalientSleepNet: Multimodal Salient Wave Detection Network for Sleep Staging

Ziyu Jia
Youfang Lin
Jing Wang
Xuehui Wang
Peiyi Xie
Yingbin Zhang

Sleep staging is fundamental for sleep assessment and disease diagnosis. Although previous attempts to classify sleep stages have achieved high classification performance, several challenges remain open: 1) How to effectively extract salient waves in multimodal sleep data; 2) How to capture the multi-scale transition rules among sleep stages; 3) How to adaptively seize the key role of specific modality for sleep staging. To address these challenges, we propose SalientSleepNet, a multimodal salient wave detection network for sleep staging. Specifically, SalientSleepNet is a temporal fully convolutional network based on the $U^2$-Net architecture that is originally proposed for salient object detection in computer vision. It is mainly composed of two independent $U^2$-like streams to extract the salient features from multimodal data, respectively. Meanwhile, the multi-scale extraction module is designed to capture multi-scale transition rules among sleep stages. Besides, the multimodal attention module is proposed to adaptively capture valuable information from multimodal data for the specific sleep stage. Experiments on the two datasets demonstrate that SalientSleepNet outperforms the state-of-the-art baselines. It is worth noting that this model has the least amount of parameters compared with the existing deep neural network models.

PDF Details DOI

IJCAI Conference 2020 Conference Paper

GraphSleepNet: Adaptive Spatial-Temporal Graph Convolutional Networks for Sleep Stage Classification

Ziyu Jia
Youfang Lin
Jing Wang
Ronghao Zhou
Xiaojun Ning
Yuanlai He
Yaoshuai Zhao

Sleep stage classification is essential for sleep assessment and disease diagnosis. However, how to effectively utilize brain spatial features and transition information among sleep stages continues to be challenging. In particular, owing to the limited knowledge of the human brain, predefining a suitable spatial brain connection structure for sleep stage classification remains an open question. In this paper, we propose a novel deep graph neural network, named GraphSleepNet, for automatic sleep stage classification. The main advantage of the GraphSleepNet is to adaptively learn the intrinsic connection among different electroencephalogram (EEG) channels, represented by an adjacency matrix, thereby best serving the spatial-temporal graph convolution network (ST-GCN) for sleep stage classification. Meanwhile, the ST-GCN consists of graph convolutions for extracting spatial features and temporal convolutions for capturing the transition rules among sleep stages. Experiments on the Montreal Archive of Sleep Studies (MASS) dataset demonstrate that the GraphSleepNet outperforms the state-of-the-art baselines.

PDF Details DOI