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Yu Qi

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19 papers

2 author rows

JBHI Journal 2025 Journal Article

A Habenula Neural Biomarker Simultaneously Tracks Weekly and Daily Symptom Variations During Deep Brain Stimulation Therapy for Depression

Shi Liu
Yu Qi
Shaohua Hu
Ning Wei
Jianmin Zhang
Junming Zhu
Hemmings Wu
Hailan Hu

Objective: Deep brain stimulation (DBS) targeting the lateral habenula (LHb) is a promising therapy for treatment-resistant depression (TRD) but its clinical effect has been variable, which can be improved by adaptive DBS (aDBS) guided by a neural biomarker of depression symptoms. Existing neural biomarkers, however, cannot simultaneously track slow and fast symptom dynamics, do not sufficiently respond to stimulation parameters, and lack neurobiological interpretability, which hinder their use in developing aDBS. Methods: We conducted a study on one TRD patient who achieved remission following a 41-week LHb DBS treatment, during which we assessed slow symptom variations using weekly clinical ratings and fast variations using daily self-reports. We recorded daily LHb local field potentials (LFP) concurrently with the reports during the entire treatment process. We then used machine learning methods to identify a personalized depression neural biomarker from spectral and temporal LFP features. Results: The neural biomarker was identified from classification of high and low depression symptom states with a cross-validated accuracy of 0. 97. It further simultaneously tracked both weekly (slow) and daily (fast) depression symptom variation dynamics, achieving test data explained variance of 0. 74 and 0. 63 respectively and responded to DBS frequency alterations. Finally, it can be neurobiologically interpreted as indicating LHb excitatory and inhibitory balance changes during DBS treatment. Conclusion: By collecting and analyzing a unique personalized dataset of weekly and daily LFP recordings and symptom evaluations, we identified a high-performance neural biomarker for depression during LHb DBS. Significance: Our results hold promise to facilitate future aDBS for treating TRD.

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AAAI Conference 2025 Conference Paper

Cauchy Diffusion: A Heavy-tailed Denoising Diffusion Probabilistic Model for Speech Synthesis

Qi Lian
Yu Qi
Yueming Wang

Denoising diffusion probabilistic models (DDPMs) have gained popularity in devising neural vocoders and obtained outstanding performance. However, existing DDPM-based neural vocoders struggle to handle the prosody diversities due to their susceptibility to mode-collapse issues confronted with imbalanced data. We introduced Cauchy Diffusion, a model incorporating the Cauchy noises to address this challenge. The heavy-tailed Cauchy distribution exhibits better resilience to imbalanced speech data, potentially improving prosody modeling. Our experiments on the LJSpeech and VCTK datasets demonstrate that Cauchy Diffusion achieved state-of-the-art speech synthesis performance. Compared to existing neural vocoders, our Cauchy Diffusion notably improved speech diversity while maintaining superior speech quality. Remarkably, Cauchy Diffusion surpassed neural vocoders based on generative adversarial networks (GANs) that are explicitly optimized to improve diversity.

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NeurIPS Conference 2025 Conference Paper

CRRL: Learning Channel-invariant Neural Representations for High-performance Cross-day Decoding

Xianhan Tan
Binli Luo
Yu Qi
Yueming Wang

Brain-computer interfaces have shown great potential in motor and speech rehabilitation, but still suffer from low performance stability across days, mostly due to the instabilities in neural signals. These instabilities, partially caused by neuron deaths and electrode shifts, leading to channel-level variabilities among different recording days. Previous studies mostly focused on aligning multi-day neural signals of onto a low-dimensional latent manifold to reduce the variabilities, while faced with difficulties when neural signals exhibit significant drift. Here, we propose to learn a channel-level invariant neural representation to address the variabilities in channels across days. It contains a channel-rearrangement module to learn stable representations against electrode shifts, and a channel reconstruction module to handle the missing neurons. The proposed method achieved the state-of-the-art performance with cross-day decoding tasks over two months, on multiple benchmark BCI datasets. The proposed approach showed good generalization ability that can be incorporated to different neural networks.

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AAAI Conference 2025 Conference Paper

DeCorrNet: Enhancing Neural Decoding Performance by Eliminating Correlations in Noise

Xianhan Tan
Yu Qi
Yueming Wang

Neural decoding, which transforms neural signals into motor commands, plays a key role in brain-computer interfaces (BCIs). Existing neural decoding approaches mainly rely on the assumption of independent noises, which could perform poorly in case the assumption is invalid. However, correlations in noises have been commonly observed in neural signals. Specifically, noise in different neural channels can be similar or highly related, which could degrade the performance of those neural decoders. To tackle this problem, we propose the DeCorrNet, which explicitly removes noise correlation in neural decoding. DeCorrNet could incorporate diverse neural decoders as an ensemble module to enhance the neural decoding performance. Experiments with benchmark BCI datasets demonstrated the superiority of DeCorrNet and achieved state-of-the-art results.

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JBHI Journal 2025 Journal Article

Dynamic Instance-Level Graph Learning Network of Intracranial Electroencephalography Signals for Epileptic Seizure Prediction

Qi Lian
Yueming Wang
Yu Qi

Brain-computer interface (BCI) technology is emerging as a valuable tool for diagnosing and treating epilepsy, with deep learning-based feature extraction methods demonstrating remarkable progress in BCI-aided systems. However, accurately identifying causal relationships in temporal dynamics of epileptic intracranial electroencephalography (iEEG) signals remains a challenge. This paper proposes a Dynamic Instance-level Graph Learning Network (DIGLN) for seizure prediction using iEEG signals. The DIGLN comprises two core components: a grouped temporal neural network that extracts node features and a graph structure learning method to capture the causality from intra-channel to inter-channel. Furthermore, we propose a graphical interactive writeback technique to enable DIGLN to capture the causality from inter-channel to intra-channel. Consequently, our DIGLN enables patient-specific dynamic instance-level graph learning, facilitating the modelling of evolving signals and functional connectivities through end-to-end data-driven learning. Experimental results on the Freiburg iEEG dataset demonstrate the superior performance of DIGLN, surpassing other deep learning-based seizure prediction methods. Visualization results further confirm DIGLN’s capability to learn interpretable and diverse connections.

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ICML Conference 2025 Conference Paper

Flow Matching for Few-Trial Neural Adaptation with Stable Latent Dynamics

Puli Wang
Yu Qi
Yueming Wang 0001
Gang Pan 0001

The primary goal of brain-computer interfaces (BCIs) is to establish a direct linkage between neural activities and behavioral actions via neural decoders. Due to the nonstationary property of neural signals, BCIs trained on one day usually obtain degraded performance on other days, hindering the user experience. Existing studies attempted to address this problem by aligning neural signals across different days. However, these neural adaptation methods may exhibit instability and poor performance when only a few trials are available for alignment, limiting their practicality in real-world BCI deployment. To achieve efficient and stable neural adaptation with few trials, we propose Flow-Based Distribution Alignment (FDA), a novel framework that utilizes flow matching to learn flexible neural representations with stable latent dynamics, thereby facilitating source-free domain alignment through likelihood maximization. The latent dynamics of FDA framework is theoretically proven to be stable using Lyapunov exponents, allowing for robust adaptation. Further experiments across multiple motor cortex datasets demonstrate the superior performance of FDA, achieving reliable results with fewer than five trials. Our FDA approach offers a novel and efficient solution for few-trial neural data adaptation, offering significant potential for improving the long-term viability of real-world BCI applications.

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ICML Conference 2025 Conference Paper

MME-CoT: Benchmarking Chain-of-Thought in Large Multimodal Models for Reasoning Quality, Robustness, and Efficiency

Dongzhi Jiang
Renrui Zhang
Ziyu Guo
Yanwei Li
Yu Qi
Xinyan Chen 0001
Liuhui Wang
Jianhan Jin

Answering questions with Chain-of-Thought (CoT) has significantly enhanced the reasoning capabilities of Large Language Models (LLMs), yet its impact on Large Multimodal Models (LMMs) still lacks a systematic assessment and in-depth investigation. In this paper, we introduce MME-CoT, a specialized benchmark evaluating the CoT reasoning performance of LMMs, spanning six domains: math, science, OCR, logic, space-time, and general scenes. As the first comprehensive study in this area, we propose a thorough evaluation suite incorporating three novel metrics that assess the reasoning quality, robustness, and efficiency at a fine-grained level. Leveraging curated high-quality data and a unique evaluation strategy, we conduct an in-depth analysis of state-of-the-art LMMs, uncovering several key insights: 1) Models with reflection mechanism demonstrate a superior CoT quality, with Kimi k1. 5 outperforming GPT-4o and demonstrating the highest quality results; 2) CoT prompting often degrades LMM performance on perception-heavy tasks, suggesting a potentially harmful overthinking behavior; and 3) Although the CoT quality is high, LMMs with reflection exhibit significant inefficiency in both normal response and self-correction phases. We hope MME-CoT serves as a foundation for advancing multimodal reasoning in LMMs.

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AAAI Conference 2024 Conference Paper

Bridging the Semantic Latent Space between Brain and Machine: Similarity Is All You Need

Jiaxuan Chen
Yu Qi
Yueming Wang
Gang Pan

How our brain encodes complex concepts has been a longstanding mystery in neuroscience. The answer to this problem can lead to new understandings about how the brain retrieves information in large-scale data with high efficiency and robustness. Neuroscience studies suggest the brain represents concepts in a locality-sensitive hashing (LSH) strategy, i.e., similar concepts will be represented by similar responses. This finding has inspired the design of similarity-based algorithms, especially in contrastive learning. Here, we hypothesize that the brain and large neural network models, both using similarity-based learning rules, could contain a similar semantic embedding space. To verify that, this paper proposes a functional Magnetic Resonance Imaging (fMRI) semantic learning network named BrainSem, aimed at seeking a joint semantic latent space that bridges the brain and a Contrastive Language-Image Pre-training (CLIP) model. Given that our perception is inherently cross-modal, we introduce a fuzzy (one-to-many) matching loss function to encourage the models to extract high-level semantic components from neural signals. Our results claimed that using only a small set of fMRI recordings for semantic space alignment, we could obtain shared embedding valid for unseen categories out of the training set, which provided potential evidence for the semantic representation similarity between the brain and large neural networks. In a zero-shot classification task, our BrainSem achieves an 11.6% improvement over the state-of-the-art.

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AAAI Conference 2023 Conference Paper

Exploring Stochastic Autoregressive Image Modeling for Visual Representation

Yu Qi
Fan Yang
Yousong Zhu
Yufei Liu
Liwei Wu
Rui Zhao
Wei Li

Autoregressive language modeling (ALM) has been successfully used in self-supervised pre-training in Natural language processing (NLP). However, this paradigm has not achieved comparable results with other self-supervised approaches in computer vision (e.g., contrastive learning, masked image modeling). In this paper, we try to find the reason why autoregressive modeling does not work well on vision tasks. To tackle this problem, we fully analyze the limitation of visual autoregressive methods and proposed a novel stochastic autoregressive image modeling (named SAIM) by the two simple designs. First, we serialize the image into patches. Second, we employ the stochastic permutation strategy to generate an effective and robust image context which is critical for vision tasks. To realize this task, we create a parallel encoder-decoder training process in which the encoder serves a similar role to the standard vision transformer focusing on learning the whole contextual information, and meanwhile the decoder predicts the content of the current position so that the encoder and decoder can reinforce each other. Our method significantly improves the performance of autoregressive image modeling and achieves the best accuracy (83.9%) on the vanilla ViT-Base model among methods using only ImageNet-1K data. Transfer performance in downstream tasks also shows that our model achieves competitive performance. Code is available at https://github.com/qiy20/SAIM.

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AAAI Conference 2023 Conference Paper

Extracting Semantic-Dynamic Features for Long-Term Stable Brain Computer Interface

Tao Fang
Qian Zheng
Yu Qi
Gang Pan

Brain-computer Interface (BCI) builds a neural signal to the motor command pathway, which is a prerequisite for the realization of neural prosthetics. However, a long-term stable BCI suffers from the neural data drift across days while retraining the BCI decoder is expensive and restricts its application scenarios. Recent solutions of neural signal recalibration treat the continuous neural signals as discrete, which is less effective in temporal feature extraction. Inspired by the observation from biologists that low-dimensional dynamics could describe high-dimensional neural signals, we model the underlying neural dynamics and propose a semantic-dynamic feature that represents the semantics and dynamics in a shared feature space facilitating the BCI recalibration. Besides, we present the joint distribution alignment instead of the common used marginal alignment strategy, dealing with the various complex changes in neural data distribution. Our recalibration approach achieves state-of-the-art performance on the real neural data of two monkeys in both classification and regression tasks. Our approach is also evaluated on a simulated dataset, which indicates its robustness in dealing with various common causes of neural signal instability.

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ICML Conference 2023 Conference Paper

Rethinking Visual Reconstruction: Experience-Based Content Completion Guided by Visual Cues

Jiaxuan Chen 0007
Yu Qi
Gang Pan 0001

Decoding seen images from brain activities has been an absorbing field. However, the reconstructed images still suffer from low quality with existing studies. This can be because our visual system is not like a camera that ”remembers” every pixel. Instead, only part of the information can be perceived with our selective attention, and the brain ”guesses” the rest to form what we think we see. Most existing approaches ignored the brain completion mechanism. In this work, we propose to reconstruct seen images with both the visual perception and the brain completion process, and design a simple, yet effective visual decoding framework to achieve this goal. Specifically, we first construct a shared discrete representation space for both brain signals and images. Then, a novel self-supervised token-to-token inpainting network is designed to implement visual content completion by building context and prior knowledge about the visual objects from the discrete latent space. Our approach improved the quality of visual reconstruction significantly and achieved state-of-the-art.

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NeurIPS Conference 2022 Conference Paper

Online Neural Sequence Detection with Hierarchical Dirichlet Point Process

Weihan Li
Yu Qi
Gang Pan

Neural sequence detection plays a vital role in neuroscience research. Recent impressive works utilize convolutive nonnegative matrix factorization and Neyman-Scott process to solve this problem. However, they still face two limitations. Firstly, they accommodate the entire dataset into memory and perform iterative updates of multiple passes, which can be inefficient when the dataset is large or grows frequently. Secondly, they rely on the prior knowledge of the number of sequence types, which can be impractical with data when the future situation is unknown. To tackle these limitations, we propose a hierarchical Dirichlet point process model for efficient neural sequence detection. Instead of computing the entire data, our model can sequentially detect sequences in an online unsupervised manner with Particle filters. Besides, the Dirichlet prior enables our model to automatically introduce new sequence types on the fly as needed, thus avoiding specifying the number of types in advance. We manifest these advantages on synthetic data and neural recordings from songbird higher vocal center and rodent hippocampus.

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NeurIPS Conference 2022 Conference Paper

Tracking Functional Changes in Nonstationary Signals with Evolutionary Ensemble Bayesian Model for Robust Neural Decoding

Xinyun Zhu
Yu Qi
Gang Pan
Yueming Wang

Neural signals are typical nonstationary data where the functional mapping between neural activities and the intentions (such as the velocity of movements) can occasionally change. Existing studies mostly use a fixed neural decoder, thus suffering from an unstable performance given neural functional changes. We propose a novel evolutionary ensemble framework (EvoEnsemble) to dynamically cope with changes in neural signals by evolving the decoder model accordingly. EvoEnsemble integrates evolutionary computation algorithms in a Bayesian framework where the fitness of models can be sequentially computed with their likelihoods according to the incoming data at each time slot, which enables online tracking of time-varying functions. Two strategies of evolve-at-changes and history-model-archive are designed to further improve efficiency and stability. Experiments with simulations and neural signals demonstrate that EvoEnsemble can track the changes in functions effectively thus improving the accuracy and robustness of neural decoding. The improvement is most significant in neural signals with functional changes.

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NeurIPS Conference 2020 Conference Paper

Reconstructing Perceptive Images from Brain Activity by Shape-Semantic GAN

Tao Fang
Yu Qi
Gang Pan

Reconstructing seeing images from fMRI recordings is an absorbing research area in neuroscience and provides a potential brain-reading technology. The challenge lies in that visual encoding in brain is highly complex and not fully revealed. Inspired by the theory that visual features are hierarchically represented in cortex, we propose to break the complex visual signals into multi-level components and decode each component separately. Specifically, we decode shape and semantic representations from the lower and higher visual cortex respectively, and merge the shape and semantic information to images by a generative adversarial network (Shape-Semantic GAN). This 'divide and conquer' strategy captures visual information more accurately. Experiments demonstrate that Shape-Semantic GAN improves the reconstruction similarity and image quality, and achieves the state-of-the-art image reconstruction performance.

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NeurIPS Conference 2019 Conference Paper

Dynamic Ensemble Modeling Approach to Nonstationary Neural Decoding in Brain-Computer Interfaces

Yu Qi
Bin Liu
Yueming Wang
Gang Pan

Brain-computer interfaces (BCIs) have enabled prosthetic device control by decoding motor movements from neural activities. Neural signals recorded from cortex exhibit nonstationary property due to abrupt noises and neuroplastic changes in brain activities during motor control. Current state-of-the-art neural signal decoders such as Kalman filter assume fixed relationship between neural activities and motor movements, thus will fail if this assumption is not satisfied. We propose a dynamic ensemble modeling (DyEnsemble) approach that is capable of adapting to changes in neural signals by employing a proper combination of decoding functions. The DyEnsemble method firstly learns a set of diverse candidate models. Then, it dynamically selects and combines these models online according to Bayesian updating mechanism. Our method can mitigate the effect of noises and cope with different task behaviors by automatic model switching, thus gives more accurate predictions. Experiments with neural data demonstrate that the DyEnsemble method outperforms Kalman filters remarkably, and its advantage is more obvious with noisy signals.

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IJCAI Conference 2019 Conference Paper

Locate-Then-Detect: Real-time Web Attack Detection via Attention-based Deep Neural Networks

Tianlong Liu
Yu Qi
Liang Shi
Jianan Yan

Web attacks such as Cross-Site Scripting and SQL Injection are serious Web threats that lead to catastrophic data leaking and loss. Because attack payloads are often short segments hidden in URL requests/posts that can be very long, classical machine learning approaches have difficulties in learning useful patterns from them. In this study, we propose a novel Locate-Then-Detect (LTD) system that can precisely detect Web threats in real-time by using attention-based deep neural networks. Firstly, an efficient Payload Locating Network (PLN) is employed to propose most suspicious regions from large URL requests/posts. Then a Payload Classification Network (PCN) is adopted to accurately classify malicious regions from suspicious candidates. In this way, PCN can focus more on learning malicious segments and highly increase detection accuracy. The noise induced by irrelevant background strings can be largely eliminated. Besides, LTD can greatly reduce computational costs (82. 6% less) by ignoring large irrelevant URL content. Experiments are carried out on both benchmarks and real Web traffic. The LTD outperforms an HMM-based approach, the Libinjection system, and a leading commercial rule-based Web Application Firewall. Our method can be efficiently implemented on GPUs with an average detection time of about 5ms and well qualified for real-time applications.

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IJCAI Conference 2018 Conference Paper

CSNN: An Augmented Spiking based Framework with Perceptron-Inception

Qi Xu
Yu Qi
Hang Yu
Jiangrong Shen
Huajin Tang
Gang Pan

Spiking Neural Networks (SNNs) represent and transmit information in spikes, which is considered more biologically realistic and computationally powerful than the traditional Artificial Neural Networks. The spiking neurons encode useful temporal information and possess highly anti-noise property. The feature extraction ability of typical SNNs is limited by shallow structures. This paper focuses on improving the feature extraction ability of SNNs in virtue of powerful feature extraction ability of Convolutional Neural Networks (CNNs). CNNs can extract abstract features resorting to the structure of the convolutional feature maps. We propose a CNN-SNN (CSNN) model to combine feature learning ability of CNNs with cognition ability of SNNs. The CSNN model learns the encoded spatial temporal representations of images in an event-driven way. We evaluate the CSNN model on the handwritten digits images dataset MNIST and its variational databases. In the presented experimental results, the proposed CSNN model is evaluated regarding learning capabilities, encoding mechanisms, robustness to noisy stimuli and its classification performance. The results show that CSNN behaves well compared to other cognitive models with significantly fewer neurons and training samples. Our work brings more biological realism into modern image classification models, with the hope that these models can inform how the brain performs this high-level vision task.

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IJCAI Conference 2018 Conference Paper

Jointly Learning Network Connections and Link Weights in Spiking Neural Networks

Yu Qi
Jiangrong Shen
Yueming Wang
Huajin Tang
Hang Yu
Zhaohui Wu
Gang Pan

Spiking neural networks (SNNs) are considered to be biologically plausible and power-efficient on neuromorphic hardware. However, unlike the brain mechanisms, most existing SNN algorithms have fixed network topologies and connection relationships. This paper proposes a method to jointly learn network connections and link weights simultaneously. The connection structures are optimized by the spike-timing-dependent plasticity (STDP) rule with timing information, and the link weights are optimized by a supervised algorithm. The connection structures and the weights are learned alternately until a termination condition is satisfied. Experiments are carried out using four benchmark datasets. Our approach outperforms classical learning methods such as STDP, Tempotron, SpikeProp, and a state-of-the-art supervised algorithm. In addition, the learned structures effectively reduce the number of connections by about 24%, thus facilitate the computational efficiency of the network.

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IS Journal 2015 Journal Article

A Cauchy-Based State-Space Model for Seizure Detection in EEG Monitoring Systems

Yueming Wang
Yu Qi
Junming Zhu
Jianmin Zhang
Yiwen Wang
Gang Pan
Xiaoxiang Zheng
Zhaohui Wu

This article proposes a state-space model with Cauchy observation noise (SSMC) to detect seizure onset in a long-term EEG monitoring system. Facing the challenge of high false detection rates (FDRs) in many existing methods caused by impulsive EOG/EMG artifacts, the SSMC uses a nonlinear state-space model to encode the gradual changes of epileptic seizure signals and reject abrupt changes brought by artifacts. The Cauchy distribution is proposed to model impulsive observation noises because this heavy-tailed distribution is better at capturing abrupt changes in noise than Gaussian, thus reducing false alarms. Experiments are carried out on a dataset collected from an EEG-monitoring brain-machine interface system that contains 10 patients and 367 hours of EEG data. The authors' method achieves a high sensitivity of 100 percent with a low FDR of 0. 08 per hour and a median time delay of 8. 10 seconds, demonstrating the method's effectiveness.

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