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Shuai Huang

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9 papers

2 author rows

EAAI Journal 2026 Journal Article

Neural network-driven shape representation and computational particle mechanics via signed distance fields

Chenghao Li
Zhengshou Lai
Shuai Huang
Linchong Huang

This study presents a framework for shape representation and computational particle mechanics of granular materials using neural network-encoded signed distance fields. The approach leverages a neural network to learn and represent a signed distance field, mapping spatial points to their signed distance from the particle surface. Two neural network models are explored: one incorporating a latent code to capture shape variations, and the other without such encoding. The accuracy of these models in capturing particle morphology is rigorously evaluated, and their ability to generate new particles with realistic shapes is demonstrated. The proposed neural network-based approach is seamlessly integrated into the signed distance field-based discrete element method, enabling efficient and robust modeling of granular particles with arbitrary shapes. The integration is validated through discrete element-based simulations, demonstrating its effectiveness in particle mechanics applications. Additionally, memory consumption and computational performance are analyzed. These contributions position the neural network-encoded signed distance fields framework as a versatile and powerful tool for advancing computational modeling of granular materials.

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EAAI Journal 2025 Journal Article

A dual-branch generative adversarial network with self-supervised enhancement for robust auditory attention decoding

Shuai Huang
Yongxiong Wang
Huan Luo

Detecting auditory attention from brain signals remains a significant challenge in neuroscience and brain–computer interface research. While progress has been made in EEG(electroencephalogram)-based auditory attention detection, existing methods often struggle with three key challenges: signal variability across subjects and sessions, limited availability of labeled training data, and performance degradation with short decision windows. In this paper, we propose a novel artificial intelligence framework for robust auditory attention decoding from EEG signals. This artificial intelligence solution implements advanced deep learning techniques for both neural signal processing and practical attention detection applications in hearing assistance devices, cognitive monitoring, and adaptive brain–computer interfaces, our Artificial Intelligence approach introduces four key innovations: (1) A dual-branch architecture that combines temporal attention and frequency residual learning, enabling comprehensive feature extraction; (2) Domain-specific generative adversarial networks designed to generate high-quality augmented samples in both temporal and frequency domains; (3) Integrated attention mechanisms and graph convolution operations to capture complex spatial–temporal–spectral relationships; and (4) A multi-task self-supervised strategy for complementary feature learning. Extensive experiments on two public EEG datasets (one with 16 subjects listening to Dutch narratives and another with 18 subjects listening to Danish audiobooks) demonstrate that our model (containing 0. 91 megabyte parameters with 2. 4 millisecond inference time) significantly outperforms state-of-the-art methods, particularly for short decision windows, achieving up to 88. 9% accuracy on 0. 1 s windows. The model shows robust performance across subjects with 76. 5% cross-dataset accuracy and maintains 76. 3% accuracy under 0 decibel signal-to-noise ratio conditions, demonstrating strong resilience to various noise conditions and graph masking strategies.

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NeurIPS Conference 2025 Conference Paper

NEED: Cross-Subject and Cross-Task Generalization for Video and Image Reconstruction from EEG Signals

Shuai Huang
Huan Luo
Haodong Jing
Qixian Zhang
Litao Chang
Yating Feng
Xiao Lin
Chendong Qin

Translating brain activity into meaningful visual content has long been recognized as a fundamental challenge in neuroscience and brain-computer interface research. Recent advances in EEG-based neural decoding have shown promise, yet two critical limitations remain in this area: poor generalization across subjects and constraints to specific visual tasks. We introduce NEED, the first unified framework achieving zero-shot cross-subject and cross-task generalization for EEG-based visual reconstruction. Our approach addresses three fundamental challenges: (1) cross-subject variability through an Individual Adaptation Module pretrained on multiple EEG datasets to normalize subject-specific patterns, (2) limited spatial resolution and complex temporal dynamics via a dual-pathway architecture capturing both low-level visual dynamics and high-level semantics, and (3) task specificity constraints through a unified inference mechanism adaptable to different visual domains. For video reconstruction, NEED achieves better performance than existing methods. Importantly, Our model maintains 93. 7% of within-subject classification performance and 92. 4% of visual reconstruction quality when generalizing to unseen subjects, while achieving an SSIM of 0. 352 when transferring directly to static image reconstruction without fine-tuning, demonstrating how neural decoding can move beyond subject and task boundaries toward truly generalizable brain-computer interfaces.

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IROS Conference 2021 Conference Paper

Multi-layer VI-GNSS Global Positioning Framework with Numerical Solution aided MAP Initialization

Bing Han
Zhongyang Xiao
Shuai Huang
Tao Zhang 0023

Motivated by the goal of achieving long-term drift-free camera pose estimation in complex scenarios, we propose a global positioning framework fusing visual, inertial and Global Navigation Satellite System (GNSS) measurements in multiple layers. Different from previous loosely- and tightly-coupled methods, the proposed multi-layer fusion allows us to delicately correct the drift of visual odometry and keep reliable positioning while GNSS degrades. In particular, local motion estimation is conducted in the inner-layer, solving the problem of scale drift and inaccurate bias estimation in visual odometry by fusing the velocity of GNSS, pre-integration of Inertial Measurement Unit (IMU) and camera measurement in a tightly-coupled way. The global localization is achieved in the outer-layer, where the local motion is further fused with GNSS position and course in a long-term period in a loosely-coupled way. Furthermore, a dedicated initialization method is proposed to guarantee fast and accurate estimation for all state variables and parameters. We give exhaustive tests of the proposed framework on indoor and outdoor public datasets. The mean localization error is reduced up to 63%, with a promotion of 69% in initialization accuracy compared with state-of-the-art works. We have applied the algorithm to Augmented Reality (AR) navigation, crowd sourcing high-precision map update and other large-scale applications.

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AAAI Conference 2018 Short Paper

Predicting Depression Severity by Multi-Modal Feature Engineering and Fusion

Aven Samareh
Yan Jin
Zhangyang Wang
Xiangyu Chang
Shuai Huang

We present our preliminary work to determine if patient’s vocal acoustic, linguistic, and facial patterns could predict clinical ratings of depression severity, namely Patient Health Questionnaire depression scale (PHQ-8). We proposed a multi-modal fusion model that combines three different modalities: audio, video, and text features. By training over the AVEC2017 dataset, our proposed model outperforms each single-modality prediction model, and surpasses the dataset baseline with a nice margin.

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IJCAI Conference 2017 Conference Paper

Doubly Sparsifying Network

Zhangyang Wang
Shuai Huang
Jiayu Zhou
Thomas S. Huang

We propose the doubly sparsifying network (DSN), by drawing inspirations from the double sparsity model for dictionary learning. DSN emphasizes the joint utilization of both the problem structure and the parameter structure. It simultaneously sparsifies the output features and the learned model parameters, under one unified framework. DSN enjoys intuitive model interpretation, compact model size and low complexity. We compare DSN against a few carefully-designed baselines, to verify its consistently superior performance in a wide range of settings. Encouraged by its robustness to insufficient training data, we explore the applicability of DSN in brain signal processing that has been a challenging interdisciplinary area. DSN is evaluated for two mainstream tasks, electroencephalographic (EEG) signal classification and blood oxygenation level dependent (BOLD) response prediction, both achieving promising results.

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NeurIPS Conference 2011 Conference Paper

Identifying Alzheimer's Disease-Related Brain Regions from Multi-Modality Neuroimaging Data using Sparse Composite Linear Discrimination Analysis

Shuai Huang
Jing Li
Jieping Ye
Teresa Wu
Kewei Chen
Adam Fleisher
Eric Reiman

Diagnosis of Alzheimer's disease (AD) at the early stage of the disease development is of great clinical importance. Current clinical assessment that relies primarily on cognitive measures proves low sensitivity and specificity. The fast growing neuroimaging techniques hold great promise. Research so far has focused on single neuroimaging modalities. However, as different modalities provide complementary measures for the same disease pathology, fusion of multi-modality data may increase the statistical power in identification of disease-related brain regions. This is especially true for early AD, at which stage the disease-related regions are most likely to be weak-effect regions that are difficult to be detected from a single modality alone. We propose a sparse composite linear discriminant analysis model (SCLDA) for identification of disease-related brain regions of early AD from multi-modality data. SCLDA uses a novel formulation that decomposes each LDA parameter into a product of a common parameter shared by all the modalities and a parameter specific to each modality, which enables joint analysis of all the modalities and borrowing strength from one another. We prove that this formulation is equivalent to a penalized likelihood with non-convex regularization, which can be solved by the DC ((difference of convex functions) programming. We show that in using the DC programming, the property of the non-convex regularization in terms of preserving weak-effect features can be nicely revealed. We perform extensive simulations to show that SCLDA outperforms existing competing algorithms on feature selection, especially on the ability for identifying weak-effect features. We apply SCLDA to the Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) images of 49 AD patients and 67 normal controls (NC). Our study identifies disease-related brain regions consistent with findings in the AD literature.

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YNIMG Journal 2010 Journal Article

Learning brain connectivity of Alzheimer's disease by sparse inverse covariance estimation

Shuai Huang
Jing Li
Liang Sun
Jieping Ye
Adam Fleisher
Teresa Wu
Kewei Chen
Eric Reiman

Rapid advances in neuroimaging techniques provide great potentials for study of Alzheimer's disease (AD). Existing findings have shown that AD is closely related to alteration in the functional brain network, i. e. , the functional connectivity between different brain regions. In this paper, we propose a method based on sparse inverse covariance estimation (SICE) to identify functional brain connectivity networks from PET data. Our method is able to identify both the connectivity network structure and strength for a large number of brain regions with small sample sizes. We apply the proposed method to the PET data of AD, mild cognitive impairment (MCI), and normal control (NC) subjects. Compared with NC, AD shows decrease in the amount of inter-region functional connectivity within the temporal lobe especially between the area around hippocampus and other regions and increase in the amount of connectivity within the frontal lobe as well as between the parietal and occipital lobes. Also, AD shows weaker between-lobe connectivity than within-lobe connectivity and weaker between-hemisphere connectivity, compared with NC. In addition to being a method for knowledge discovery about AD, the proposed SICE method can also be used for classifying new subjects, which makes it a suitable approach for novel connectivity-based AD biomarker identification. Our experiments show that the best sensitivity and specificity our method can achieve in AD vs. NC classification are 88% and 88%, respectively.

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NeurIPS Conference 2009 Conference Paper

Learning Brain Connectivity of Alzheimer's Disease from Neuroimaging Data

Shuai Huang
Jing Li
Liang Sun
Jun Liu
Teresa Wu
Kewei Chen
Adam Fleisher
Eric Reiman

Recent advances in neuroimaging techniques provide great potentials for effective diagnosis of Alzheimer’s disease (AD), the most common form of dementia. Previous studies have shown that AD is closely related to alternation in the functional brain network, i. e. , the functional connectivity among different brain regions. In this paper, we consider the problem of learning functional brain connectivity from neuroimaging, which holds great promise for identifying image-based markers used to distinguish Normal Controls (NC), patients with Mild Cognitive Impairment (MCI), and patients with AD. More specifically, we study sparse inverse covariance estimation (SICE), also known as exploratory Gaussian graphical models, for brain connectivity modeling. In particular, we apply SICE to learn and analyze functional brain connectivity patterns from different subject groups, based on a key property of SICE, called the “monotone property” we established in this paper. Our experimental results on neuroimaging PET data of 42 AD, 116 MCI, and 67 NC subjects reveal several interesting connectivity patterns consistent with literature findings, and also some new patterns that can help the knowledge discovery of AD.

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