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Yingwei Zhang

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10 papers

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JBHI Journal 2026 Journal Article

MSFSNet: Multi-Source Few-Shot Adaptation Network for Cross-Subject Depression Recognition from EEG Signals

Kang Wang
Yanan Zhang
Yingwei Zhang
Fa Zhang
Jian Shen
Bin Hu

Depression is a prevalent mental disorder with severe socio-economic implications, and its early identification and intervention are crucial for mitigating disease progression. However, existing machine learning and deep learning-based approaches for depression recognition exhibit limited generalization across individuals, making them less adaptable to new subjects and restricting their practical applications. To address this issue, we propose a cross-subject depression recognition method based on Multi-Source Few-Shot Adaptation (MSFSA) using electroencephalography (EEG). The proposed method integrates multi-source domain adaptation and ensemble learning strategies. Specifically, the multi-source domain adaptation module employs an alternating training mechanism combining unsupervised domain adaptation and few-shot adaptation, reducing the model's dependency on specific subjects. Meanwhile, ensemble learning improves model robustness and stability by aggregating multiple model predictions, reducing the impact of individual model biases and enhancing classification reliability. Experiments were conducted on the public MODMA EEG dataset, comprising 53 subjects (24 patients with major depressive disorder and 29 healthy controls). With a theoretical chance level of 50% for the cross-subject classification setting, the results demonstrate that, compared with traditional machine learning methods, existing EEG-based depression recognition models, and advanced domain adaptation algorithms, leveraging the Alpha and low-Gamma band features as the key contributing factors, the proposed method achieves a significant improvement in accuracy, reaching 87. 12%, which outperforms the state-of-the-art HEMAsNet (80. 67%) and WDANet (70. 94%) on the same dataset under the 10-fold cross-subject validation protocol. These findings indicate that the proposed approach effectively reduces subject dependency in EEG-based depression recognition and provides a promising solution for improving cross-subject adaptability.

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AAAI Conference 2026 Conference Paper

State Mamba: Spatiotemporal EEG State-Space Model with Dynamic Brain Alignment for Cross-Subject Representation

Weining Weng
Yang Gu
Yuan Ma
Yuchen Liu
Yingwei Zhang
Yiqiang Chen

Cross-subject EEG decoding remains a fundamental challenge due to substantial inter-subject variability in brain activity, which hinders the development of subject-independent EEG models. Despite progress in extracting cross-subject invariant features, existing studies neglect the shared neural responses that arise under similar cognitive or emotional states across individuals, limiting their ability to learn generalized and consistent EEG representations. To address the challenges, we propose State Mamba, a novel spatiotemporal EEG state-space model that explicitly models and aligns neural responses and their spatiotemporal state transitions to learn consistent and generalizable representations across subjects. Innovatively, State Mamba theoretically formulates a multi-channel Mamba architecture that jointly models spatial and temporal brain state transitions, supporting principled analysis of neural responses. To enhance spatiotemporal feature coupling, we introduce the LGANN module, which adopts global-local attention to integrate long- and short-term brain activity into a compact EEG representation. Furthermore, we design two self-supervised pretext tasks to extract consistent neural patterns across subjects: (1) representation alignment to align EEG representation, and (2) pattern alignment to align their transition rules under identical conditions, jointly promoting subject-invariant EEG representations. Extensive experiments on three benchmark datasets, FACED, DEAP, and ISRUC, demonstrate the superior performance of State Mamba in cross-subject emotion and sleep recognition tasks, validating its robust generalization capability.

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IJCAI Conference 2025 Conference Paper

CRAFT: Time Series Forecasting with Cross-Future Behavior Awareness

Yingwei Zhang
Ke Bu
Zhuoran Zhuang
Tao Xie
Yao Yu
Dong Li
Yang Guo
Detao Lv

The past decades witness the significant advancements in time series forecasting (TSF) across various real-world domains, including e-commerce and disease spread prediction. However, TSF is usually constrained by the uncertainty dilemma of predicting future data with limited past observations. To settle this question, we explore the use of Cross-Future Behavior (CFB) in TSF, which occurs before the current time but takes effect in the future. We leverage CFB features and propose the CRoss-Future Behavior Awareness based Time Series Forecasting method (CRAFT). The core idea of CRAFT is to utilize the trend of cross-future behavior to mine the trend of time series data to be predicted. Specifically, to settle the sparse and partial flaws of cross-future behavior, CRAFT employs the Koopman Predictor Module to extract the key trend and the Internal Trend Mining Module to supplement the unknown area of the cross-future behavior matrix. Then, we introduce the External Trend Guide Module with a hierarchical structure to acquire more representative trends from higher levels. Finally, we apply the demand-constrained loss to calibrate the distribution deviation of prediction results. We conduct experiments on real-world dataset. Experiments on both offline large-scale dataset and online A/B test demonstrate the effectiveness of CRAFT. Our dataset and code are available at https: //github. com/CRAFTinTSF/CRAFT.

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AAAI Conference 2025 Conference Paper

Ultra-High Resolution Segmentation via Boundary-Enhanced Patch-Merging Transformer

Haopeng Sun
Yingwei Zhang
Lumin Xu
Sheng Jin
Yiqiang Chen

Segmentation of ultra-high resolution (UHR) images is a critical task with numerous applications, yet it poses significant challenges due to high spatial resolution and rich fine details. Recent approaches adopt a dual-branch architecture, where a global branch learns long-range contextual information and a local branch captures fine details. However, they struggle to handle the conflict between global and local information while adding significant extra computational cost. Inspired by the human visual system's ability to rapidly orient attention to important areas with fine details and filter out irrelevant information, we propose a novel UHR segmentation method called Boundary-enhanced Patch-merging Transformer (BPT). BPT consists of two key components: (1) Patch-Merging Transformer (PMT) for dynamically allocating tokens to informative regions to acquire global and local representations, and (2) Boundary-Enhanced Module (BEM) that leverages boundary information to enrich fine details. Extensive experiments on multiple UHR image segmentation benchmarks demonstrate that our BPT outperforms previous state-of-the-art methods without introducing extra computational overhead.

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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.

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IJCAI Conference 2024 Conference Paper

FedES: Federated Early-Stopping for Hindering Memorizing Heterogeneous Label Noise

Bixiao Zeng
Xiaodong Yang
Yiqiang Chen
Zhiqi Shen
Hanchao Yu
Yingwei Zhang

Federated learning (FL) facilitates collaborative model training across distributed clients while maintaining privacy. Federated noisy label learning (FNLL) is more of a challenge for data inaccessibility and noise heterogeneity. Existing works primarily assume clients are either noisy or clean, which may lack the flexibility to adapt to diverse label noise across different clients, especially when entirely clean or noisy clients are not the majority. To address this, we propose a general noise-robust federated learning framework called Federated Early-Stopping (FedES), which adaptively updates critical parameters of each local model based on their noise rates, thereby avoiding overfitting to noisy labels. FedES is composed of two stages: federated noise estimation and parameter-adaptive local updating \& global aggregation. We introduce a signed distance based on local and global gradients during a federated round to estimate clients' noise rates without requiring additional information. Based on this measure, we employ various degrees of early-stopping during local updating on the clients, and further, a noise-aware global aggregation is employed to achieve noise-robust learning. Extensive experiments conducted on varying synthetic and real-world label noise demonstrate the superior performance of FedES over the state-of-the-art methods.

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AAAI Conference 2022 Short Paper

Class-Wise Adaptive Self Distillation for Federated Learning on Non-IID Data (Student Abstract)

Yuting He
Yiqiang Chen
Xiaodong Yang
Yingwei Zhang
Bixiao Zeng

Federated learning (FL) enables multiple clients to collaboratively train a globally generalized model while keeping local data decentralized. A key challenge in FL is to handle the heterogeneity of data distributions among clients. The local model will shift the global feature when fitting local data, which results in forgetting the global knowledge. Following the idea of knowledge distillation, the global model’s prediction can be utilized to help local models preserve the global knowledge in FL. However, when the global model hasn’t converged completely, its predictions tend to be less reliable on certain classes, which may results in distillation’s misleading of local models. In this paper, we propose a classwise adaptive self distillation (FedCAD) mechanism to ameliorate this problem. We design class-wise adaptive terms to soften the influence of distillation loss according to the global model’s performance on each class and therefore avoid the misleading. Experiments show that our method outperforms other state-of-the-art FL algorithms on benchmark datasets.

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TIST Journal 2022 Journal Article

CLC: A Consensus-based Label Correction Approach in Federated Learning

Bixiao Zeng
Xiaodong Yang
Yiqiang Chen
Hanchao Yu
Yingwei Zhang

Federated learning (FL) is a novel distributed learning framework where multiple participants collaboratively train a global model without sharing any raw data to preserve privacy. However, data quality may vary among the participants, the most typical of which is label noise. The incorrect label would significantly damage the performance of the global model. In FL, the inaccessibility of raw data makes this issue more challenging. Previously published studies are limited to using a task-specific benchmark-trained model to evaluate the relevance between the benchmark dataset in the server and the local one on the participants’ side. However, such approaches have failed to exploit the cooperative nature of FL itself and are not practical. This paper proposes a Consensus-based Label Correction approach (CLC) in FL, which tries to correct the noisy labels using the developed consensus method among the FL participants. The consensus-defined class-wise information is used to identify the noisy labels and correct them with pseudo-labels. Extensive experiments are conducted on several public datasets in various settings. The experimental results prove the advantage over the state-of-art methods. The link to the source code is https://github.com/bixiao-zeng/CLC.git.

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IJCAI Conference 2020 Conference Paper

Bridging Cross-Tasks Gap for Cognitive Assessment via Fine-Grained Domain Adaptation

Yingwei Zhang
Yiqiang Chen
Hanchao Yu
Zeping Lv
Qing Li
Xiaodong Yang

Discriminating pathologic cognitive decline from the expected decline of normal aging is an important research topic for elderly care and health monitoring. However, most cognitive assessment methods only work when data distributions of the training set and testing set are consistent. Enabling existing cognitive assessment models to adapt to the data in new cognitive assessment tasks is a significant challenge. In this paper, we propose a novel domain adaptation method, namely the Fine-Grained Adaptation Random Forest (FAT), to bridge the cognitive assessment gap when the data distribution is changed. FAT is composed of two essential parts 1) information gain based model evaluation strategy (IGME) and 2) domain adaptation tree growing mechanism (DATG). IGME is used to evaluate every individual tree, and DATG is used to transfer the source model to the target domain. To evaluate the performance of FAT, we conduct experiments in real clinical environments. Experimental results demonstrate that FAT is significantly more accurate and efficient compared with other state-of-the-art methods.

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AAAI Conference 2020 Conference Paper

Instance-Wise Dynamic Sensor Selection for Human Activity Recognition

Xiaodong Yang
Yiqiang Chen
Hanchao Yu
Yingwei Zhang
Wang Lu
Ruizhe Sun

Human Activity Recognition (HAR) is an important application of smart wearable/mobile systems for many humancentric problems such as healthcare. The multi-sensor synchronous measurement has shown better performance for HAR than a single sensor. However, the multi-sensor setting increases the costs of data transmission, computation and energy. Therefore, the efﬁcient sensor selection to balance recognition accuracy and sensor cost is the critical challenge. In this paper, we propose an Instance-wise Dynamic Sensor Selection (IDSS) method for HAR. Firstly, we formalize this problem as minimizing both activity classiﬁcation loss and sensor number by dynamically selecting a sparse subset for each instance. Then, IDSS solves the above minimization problem via Markov Decision Process whose policy for sensor selection is learned by exploiting the instancewise states using Imitation Learning. In order to optimize the parameters of the activity classiﬁcation model and the sensor selection policy, an algorithm named Mutual DAgger is proposed to alternatively enhance their learning process. To evaluate the performance of IDSS, we conduct experiments on three real-world HAR datasets. The experimental results show that IDSS can effectively reduce the overall sensor number without losing accuracy and outperforms the state-of-theart methods regarding the combined measurement of accuracy and sensor number.

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