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Xiaojun Ning

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4 papers

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NeurIPS Conference 2025 Conference Paper

From Indicators to Insights: Diversity-Optimized for Medical Series-Text Decoding via LLMs

Xiyuan Jin
Jing Wang
Ziwei Lin
QIANRU JIA
Yuqing Huang
Xiaojun Ning
Zhonghua Shi
Youfang Lin

Medical time-series analysis differs fundamentally from general ones by requiring specialized domain knowledge to interpret complex signals and clinical context. Large language models (LLMs) hold great promise for augmenting medical time-series analysis by complementing raw series with rich contextual knowledge drawn from biomedical literature and clinical guidelines. However, realizing this potential depends on precise and meaningful prompts that guide the LLM to key information. Yet, determining what constitutes effective prompt content remains non-trivial—especially in medical settings where signal interpretation often hinges on subtle, expert-defined decision-making indicators. To this end, we propose InDiGO, a knowledge-aware evolutionary learning framework that integrates clinical signals and decision-making indicators through iterative optimization. Across four medical benchmarks, InDiGO consistently outperforms prior methods. The code is available at: https: //github. com/jinxyBJTU/InDiGO.

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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.

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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.

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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.

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