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Yong Yang

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25 papers

2 author rows

EAAI Journal 2026 Journal Article

A dual-stream regional feature learning and adaptive fusion method for electroencephalogram-based emotion recognition

Yong Yang
Wenhao Wang
Kaibo Shi
Yuanlun Xie
Nan Zhou
Shiping Wen
Ming Zhu
Badong Chen

Electroencephalogram (EEG) has become a research hotspot in emotion recognition due to its high temporal resolution and ability to truly reflect brain activity. However, few existing EEG-based emotion recognition methods integrate brain region information into the algorithm and do not fully extract the deep features of each region. Brain science has shown that different brain regions have different functions and are highly correlated with the production of emotions. In this paper, based on the division of brain regions, a dual-branch regional feature learning and adaptive fusion neural network (DRFNet) is proposed to extract the features of different brain regions and adaptively fuse regional features, thereby achieving accurate EEG emotion recognition. Specifically, DRFNet mainly consists of regional feature extraction modules (DB-CTFEM) and a feature fusion module (RFM). The DB-CTFEM extracts regional local and global features through the dual-branch structure of convolutional neural network (CNN) and Transformer, respectively, and then uses cross-attention to effectively fuse the two to obtain enhanced regional features. Considering the differences of brain regions, RFM uses the attention mechanism to fuse regional features and adaptively reconstruct global brain features. In addition, a region loss function based on the importance of region features is proposed to dynamically adjust the contribution weights of different brain regions, thereby guiding the model to pay more attention to key regions. This paper conducts subject-dependent experiments on the SJTU Emotion EEG Datasets (SEED, SEED-IV, SEED-V, and SEED-VII) to verify effectiveness and robustness of the proposed method.

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AAAI Conference 2026 Conference Paper

HyCoRA: Hyper-Contrastive Role-Adaptive Learning for Role-Playing

Shihao Yang
Zhicong Lu
Yong Yang
Bo Lv
Yang Shen
Nayu Liu

Multi-character role-playing aims to equip models with the capability to simulate diverse roles. Existing methods either use one shared parameterized module across all roles or assign a separate parameterized module to each role. However, the role-shared module may ignore distinct traits of each role, weakening personality learning, while the role-specific module may overlook shared traits across multiple roles, hindering commonality modeling. In this paper, we propose a novel HyCoRA: Hyper-Contrastive Role-Adaptive learning framework, which efficiently improves multi-character role-playing agents' ability by balancing the learning of distinct and shared traits. Specifically, we propose a Hyper-Half Low-Rank Adaptation structure, where one half is a role-specific module generated by a lightweight hyper-network, and the other half is a trainable role-shared module. The role-specific module is devised to represent distinct persona signatures, while the role-shared module serves to capture common traits. Moreover, to better reflect distinct personalities across different roles, we design a hyper-contrastive learning mechanism to help the hyper-network distinguish their unique characteristics. Extensive experimental results on both English and Chinese available benchmarks demonstrate the superiority of our framework. Further GPT-4 evaluations and visual analyses also verify the capability of HyCoRA to capture role characteristics.

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AAAI Conference 2026 Conference Paper

UMNet: Uncertainty-guided Memory Network for Hyperspectral Pansharpening

Xiaozheng Wang
Yong Yang
Shuying Huang
Nayu Liu
Ziyang Liu

At present, most hyperspectral (HS) sharpening methods have not fully utilized the feature correlation between adjacent bands in HS images, nor have they explored the problem of feature uncertainty generated by the model during the fusion process. This may lead to inaccurate fusion features generated by the model, resulting in spatial and spectral distortions in the fusion results. To address these issues, we propose an uncertainty-guided memory network (UMNet) for HS pansharpening. A spatial-spectral recurrent fusion unit (SRFU) is designed based on the concept of temporal data modeling, which utilizes the correlation between adjacent bands to fuse spectral and spatial features from PAN and LRHS images. In SRFU, a state memory interaction unit (SMIU) is constructed based on non-negative matrix factorization (NMF) to learn the global spatial-spectral dependency of PAN and HS images in the recurrent state space. Moreover, based on uncertainty theory, we define two spatial-spectral uncertainty-guided loss functions for the HS pansharpening task to train the model step by step, ensuring that the network can reconstruct more accurate spectral and spatial features. Extensive experiments on three widely used datasets demonstrate that, compared with some state-of-the-art (SOTA) methods, the proposed UMNet has achieved significant improvements in both spatial and spectral quality metrics.

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AAAI Conference 2025 Conference Paper

AD4CD: Causal-Guided Anomaly Detection for Enhancing Cognitive Diagnosis

Haiping Ma
Yue Yao
Changqian Wang
Siyu Song
Yong Yang

Cognitive diagnosis is a key task in computer-aided education, aimed at assessing a students' proficiency in specific knowledge concepts based on their responses to exercises. However, existing cognitive diagnosis models often overlook anomalies in students and exercises. For instance, some students might incorrectly response exercises despite having a strong grasp of the knowledge concept, or they might response correctly despite a lack of understanding. Such subtle anomalies can adversely affect the diagnostic results of the models. To address these anomalies, we conduct a qualitative analysis of how anomalous student states and exercise properties impact response outcomes using causal diagrams. We propose a framework named Anomaly Detection for Cognitive Diagnosis (AD4CD) to enhance the ability of Learning-to-Detect-Anomalous. AD4CD approaches the problem from a causal perspective, analyzing confounding paths that affect the true causal relationship between student ability and response outcomes, and designing an anomaly detection mechanism suitable for cognitive diagnostic models. Specifically, we first account for anomalous student behaviors and exercise properties and introduce response times from both students and exercises as modeling factors. By quantifying the response time distributions in high-dimensional features, we identify anomalies within skewed distributions, including both left-tail and right-tail anomalies. Using the detected anomaly scores, we comprehensively model the students' anomalous behaviors and exercise anomalies. Additionally, we reconstruct unbiased true abilities under natural conditions and use reconstruction loss as an anomaly score to assist in modeling guessing and slipping features. Lastly, AD4CD leverages a general cognitive diagnosis model as its backbone, optimizing the guessing and slipping features to provide unbiased and accurate feedback. Extensive experimental results demonstrate that AD4CD effectively captures anomalous data in the diagnostic process across three real-world datasets, enhancing the accuracy of the diagnostic results.

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JBHI Journal 2025 Journal Article

Characterization of Cortical Connectivity in the Deception State With a Data-Driven Network Model Based on EEG Signal

Qianruo Kang
Yaqian Li
Xiang Li
Min Tian
Yin Xiang
Feng Li
Siyu Peng
Yijun Xiong

This study investigates the pattern of information interaction at the cortical level during deception, aiming to reveal the cognitive processes involved in the deception task. Our study involves the 64-channel EEG signals of 28 subjects (14 for innocent and 14 for guilty groups) acquired under the guilty knowledge test (GKT) lie-detection protocol. Additionally, we establish the functional connectivity network at the cortical level considering volume conduction effects, use a data-driven approach to select the regions of interest (ROIs) on the subject's cortex based on scalp electrical activity, and perform cortical current density estimation on 15 ROIs. The nonlinear dependence between the cortical waveforms of the ROIs is quantified based on mutual information, and a network of cortical mutual information connections is constructed in four frequency bands: delta, theta, alpha, and beta. The feature extraction and classification process are performed in each frequency band, and the mutual information connections statistically different between the innocent and guilty groups are first selected as features using statistical tests. Moreover, the optimal feature subset (OFS) is found by combining the SVM classifier and the wrapper feature selection strategy. Furthermore, the most important mutual information connections (MIMICs) per frequency band are obtained by refining the OFS according to the classification performance curve. The average test accuracies of MIMICs in the delta, theta, alpha, and beta bands reached 99. 76%, 96. 42%, 84. 04%, and 97. 61%, respectively. Finally, the physiological significance of each frequency sub-band and the physiological function of MIMICs are combined to explore the cognitive mechanism of lies and provide new evidence for cognitive activity in lying states.

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AAAI Conference 2025 Conference Paper

FMPM-DNet: Hyperspectral Pansharpening Dynamic Network Based on Feature Modulation and Probability Mask

Xiaozheng Wang
Yong Yang
Shuying Huang
Hangyuan Lu
Weiguo Wan
Aoqi Zhao

Currently, most Hyperspectral (HS) pansharpening methods have two problems, namely the lack of consideration the spatial variations of HS images and inaccurate feature reconstruction in multi-channel complex mapping relationships, leading to spectral and spatial distortions in the fusion results. To address these issues, we propose a dynamic network based on feature modulation and probability mask (FMPM-DNet) for HS pansharpening, including two stages of spectral-spatial feature modulation and feature reconstruction. In the first stage, to increase the feature representation ability of the model, a wave function is defined based on complex transformation to convert spatial features into wave-like features. On this basis, considering the spatial variations of HS images, a dynamic feature modulation unit (DFMU) is constructed to achieve adaptive modulation and coarse fusion of features by dynamically generating spectral-spatial correction matrix. In the second stage, a feature probability mask unit (FPMU) is designed to realize global feature embedding at different depths and local feature embedding at the same depth to obtain refined fused features. Extensive experiments on three widely used datasets demonstrate that the proposed FMPM-Net achieves significant improvements in both spatial and spectral quality metrics compared to some state-of-the-art (SOTA) methods.

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ICLR Conference 2025 Conference Paper

Image-level Memorization Detection via Inversion-based Inference Perturbation

Yue Jiang
Haokun Lin
Yang Bai
Bo Peng 0002
Zhili Liu
Yueming Lyu
Yong Yang
Xing Zheng

Recent studies have discovered that widely used text-to-image diffusion models can replicate training samples during image generation, a phenomenon known as memorization. Existing detection methods primarily focus on identifying memorized prompts. However, in real-world scenarios, image owners may need to verify whether their proprietary or personal images have been memorized by the model, even in the absence of paired prompts or related metadata. We refer to this challenge as image-level memorization detection, where current methods relying on original prompts fall short. In this work, we uncover two characteristics of memorized images after perturbing the inference procedure: lower similarity of the original images and larger magnitudes of TCNP. Building on these insights, we propose Inversion-based Inference Perturbation (IIP), a new framework for image-level memorization detection. Our approach uses unconditional DDIM inversion to derive latent codes that contain core semantic information of original images and optimizes random prompt embeddings to introduce effective perturbation. Memorized images exhibit distinct characteristics within the proposed pipeline, providing a robust basis for detection. To support this task, we construct a comprehensive setup for the image-level memorization detection, carefully curating datasets to simulate realistic memorization scenarios. Using this setup, we evaluate our IIP framework across three different memorization settings, demonstrating its state-of-the-art performance in identifying memorized images in various settings, even in the presence of data augmentation attacks.

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JBHI Journal 2025 Journal Article

TSP-OCS: A Time-Series Prediction for Optimal Camera Selection in Multi-Viewpoint Surgical Video Analysis

Xinyu Liu
Xiaoguang Lin
Xiang Liu
Yong Yang
Hongqian Wang
Qilong Sun

Recording open surgery procedures is essential for educational and clinical evaluation purposes; however, traditional single-camera methods often face challenges such as occlusions caused by the surgeon's head and body, as well as limitations due to fixed camera angles, which undermine the comprehensibility of the recorded surgical content. In this study, we specifically focus on open thyroidectomy and employ a multi-viewpoint camera recording setup, in which six synchronized cameras capture the surgery from different angles simultaneously. We develop a supervised time-series prediction framework to automatically select the most informative camera views, ensuring better coverage of critical steps. Our model forecasts camera selections by extracting and fusing visual and semantic features from thyroidectomy videos using pre-trained models, followed by temporal modeling with TimeBlocks. We constructed a dataset of five thyroidectomy procedures with synchronized six-view recordings and conducted experiments. The results show that our method achieves stable accuracy compared with existing baselines and outperforms several mainstream time-series prediction models in this specific surgical scenario. This work provides an initial exploration of multi-view camera selection for thyroidectomy, with potential value for surgical video documentation and training. Code is available at https://github.com/Aveouter/SurgicalCamSwitch.

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ECAI Conference 2024 Conference Paper

Exploring Large Language Models Text Style Transfer Capabilities

Weijie Li 0001
Zhentao Gu
Xiaochao Fan
Wenjun Deng
Yong Yang
Xinyuan Zhao
Yufeng Diao
Liang Yang 0003

The emergence of Large Language Models (LLMs) provides a new solution to text generation tasks that involve high complexity, such as text style transfer (TST) tasks. However, previous studies have not fully explored the TST capabilities of different LLMs, and have faced issues with a lack of uniform standards in the human evaluation stage. This makes the results of human evaluation difficult to reproduce and less credible. To address this, this paper designs a prompt template to guide the cutting-edge LLMs to perform effective text style transfer and carries out an in-depth comparative analysis of various small-scale language models. In the stage of human evaluation, this paper eschews the conventional rating system, opting instead for a comparative human assessment methodology, which we refer to as duel-ranking. This method determines the relative ranking of models through mutual comparison, serving as an alternative to direct scoring. Detailed evaluation instructions are provided herein, to enhance the reproducibility of this method and ensure consistency throughout the evaluation process. This manual evaluation process reveals that GPT-3. 5 and GPT-4 exhibit excellent performance in the TST tasks.

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AAAI Conference 2024 Conference Paper

MFTN: Multi-Level Feature Transfer Network Based on MRI-Transformer for MR Image Super-resolution

Shuying Huang
Ge Chen
Yong Yang
Xiaozheng Wang
Chenbin Liang

Due to the unique environment and inherent properties of magnetic resonance imaging (MRI) instruments, MR images typically have lower resolution. Therefore, improving the resolution of MR images is beneficial for assisting doctors in diagnosing the condition. Currently, the existing MR image super-resolution (SR) methods still have the problem of insufficient detail reconstruction. To overcome this issue, this paper proposes a multi-level feature transfer network (MFTN) based on MRI-Transformer to realize SR of low-resolution MRI data. MFTN consists of a multi-scale feature reconstruction network (MFRN) and a multi-level feature extraction branch (MFEB). MFRN is constructed as a pyramid structure to gradually reconstruct image features at different scales by integrating the features obtained from MFEB, and MFEB is constructed to provide detail information at different scales for low resolution MR image SR reconstruction by constructing multiple MRI-Transformer modules. Each MRI-Transformer module is designed to learn the transfer features from the reference image by establishing feature correlations between the reference image and low-resolution MR image. In addition, a contrast learning constraint item is added to the loss function to enhance the texture details of the SR image. A large number of experiments show that our network can effectively reconstruct high-quality MR Images and achieves better performance compared to some state-of-the-art methods. The source code of this work will be released on GitHub.

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JBHI Journal 2023 Journal Article

Analysis of Weight-Directed Functional Brain Networks in the Deception State Based on EEG Signal

Sihong Wei
Junfeng Gao
Yong Yang
Neal Xiong
Jiaqi Zhang
Jian Song
Qianruo Kang
Yaqian Li

Although analyzing the brain's functional and structural network has revealed that numerous brain networks are necessary to collaborate during deception, the directionality of these functional networks is still unknown. This study investigated the effective connectivity of the brain networks during deception and uncovers the information-interaction patterns of lying neural oscillations. The electroencephalography (EEG) data of 40 lying persons and 40 honest persons were used to create the weight- directed functional brain networks (WDFBN). Specifically, the connecting edge weight was defined based on the normalized phase transfer entropy (dPTE) between each electrode pair, where the network nodes involved 30 electrode channels. Additionally, the signal connectivity matrices were constructed in four frequency bands: delta, theta, alpha, and beta and were subjected to a difference analysis of entropy values between the groups. Statistical analysis of the classification results revealed that all frequency bands correctly detect deception and innocence with an accuracy of 92. 83%, 94. 17%, 85. 93%, and 92. 25%, respectively. Therefore, dPTE can be considered a valuable feature for identifying lying. According to WDFBN analysis, deception has stronger information flow in the frontoparietal, frontotemporal and temporoparietal networks compare to honest people. Furthermore, the prefrontal cortex was also found to be activated in all frequency ranges. This study examined the critical pathways of brain information interaction during deception, providing new insights into the underlying neural mechanisms. Our analysis offers significant evidence for the development of brain networks that could potentially be used for lie detection.

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NeurIPS Conference 2023 Conference Paper

Interpreting Unsupervised Anomaly Detection in Security via Rule Extraction

Ruoyu Li
Qing Li
Yu Zhang
Dan Zhao
Yong Jiang
Yong Yang

Many security applications require unsupervised anomaly detection, as malicious data are extremely rare and often only unlabeled normal data are available for training (i. e. , zero-positive). However, security operators are concerned about the high stakes of trusting black-box models due to their lack of interpretability. In this paper, we propose a post-hoc method to globally explain a black-box unsupervised anomaly detection model via rule extraction. First, we propose the concept of distribution decomposition rules that decompose the complex distribution of normal data into multiple compositional distributions. To find such rules, we design an unsupervised Interior Clustering Tree that incorporates the model prediction into the splitting criteria. Then, we propose the Compositional Boundary Exploration (CBE) algorithm to obtain the boundary inference rules that estimate the decision boundary of the original model on each compositional distribution. By merging these two types of rules into a rule set, we can present the inferential process of the unsupervised black-box model in a human-understandable way, and build a surrogate rule-based model for online deployment at the same time. We conduct comprehensive experiments on the explanation of four distinct unsupervised anomaly detection models on various real-world datasets. The evaluation shows that our method outperforms existing methods in terms of diverse metrics including fidelity, correctness and robustness.

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AAAI Conference 2023 Conference Paper

Low-Light Image Enhancement Network Based on Multi-Scale Feature Complementation

Yong Yang
Wenzhi Xu
Shuying Huang
Weiguo Wan

Images captured in low-light environments have problems of insufficient brightness and low contrast, which will affect subsequent image processing tasks. Although most current enhancement methods can obtain high-contrast images, they still suffer from noise amplification and color distortion. To address these issues, this paper proposes a low-light image enhancement network based on multi-scale feature complementation (LIEN-MFC), which is a U-shaped encoder-decoder network supervised by multiple images of different scales. In the encoder, four feature extraction branches are constructed to extract features of low-light images at different scales. In the decoder, to ensure the integrity of the learned features at each scale, a feature supplementary fusion module (FSFM) is proposed to complement and integrate features from different branches of the encoder and decoder. In addition, a feature restoration module (FRM) and an image reconstruction module (IRM) are built in each branch to reconstruct the restored features and output enhanced images. To better train the network, a joint loss function is defined, in which a discriminative loss term is designed to ensure that the enhanced results better meet the visual properties of the human eye. Extensive experiments on benchmark datasets show that the proposed method outperforms some state-of-the-art methods subjectively and objectively.

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IJCAI Conference 2023 Conference Paper

MMPN: Multi-supervised Mask Protection Network for Pansharpening

Changjie Chen
Yong Yang
Shuying Huang
Wei Tu
Weiguo Wan
Shengna Wei

Pansharpening is to fuse a panchromatic (PAN) image with a multispectral (MS) image to obtain a high-spatial-resolution multispectral (HRMS) image. The deep learning-based pansharpening methods usually apply the convolution operation to extract features and only consider the similarity of gradient information between PAN and HRMS images, resulting in the problems of edge blur and spectral distortion in the fusion results. To solve this problem, a multi-supervised mask protection network (MMPN) is proposed to prevent spatial information from being damaged and overcome spectral distortion in the learning process. Firstly, by analyzing the relationships between high-resolution images and corresponding degraded images, a mask protection strategy (MPS) for edge protection is designed to guide the recovery of fused images. Then, based on the MPS, an MMPN containing four branches is constructed to generate the fusion and mask protection images. In MMPN, each branch employs a dual-stream multi-scale feature fusion module (DMFFM), which is built to extract and fuse the features of two input images. Finally, different loss terms are defined for the four branches, and combined into a joint loss function to realize network training. Experiments on simulated and real satellite datasets show that our method is superior to state-of-the-art methods both subjectively and objectively.

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IROS Conference 2022 Conference Paper

AB-Mapper: Attention and BicNet based Multi-agent Path Planning for Dynamic Environment

Huifeng Guan
Yuan Gao 0024
Min Zhao
Yong Yang
Fuqin Deng
Tin Lun Lam

Multi-agent path finding in dynamic environments is of great academic and practical value for multi-robot systems in the real world. To improve the effectiveness and efficiency of the learning process during path planning in dynamic environments, we introduce an algorithm called Attention and BicNet based Multi-agent path planning with effective reinforcement (AB-Mapper) under the actor-critic reinforcement learning framework. In this framework, on one hand, we design an actor-network that can utilize the BicNet with communication function to achieve the intra-team coordination. On the other hand, we propose a critic network that can selectively allocate attention weights to surrounding agents. This attention mechanism allows an individual agent to automatically learn a better evaluation of actions by considering the behaviours of its surrounding agents. Compared with the SOTA method Mapper in crowded environments with dynamic obstacles, our AB-Mapper is more effective (90. 27±0. 06% vs. 61. 65±13. 90% in terms of mean success rate) in solving the general multi-agent path finding problem.

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ICRA Conference 2022 Conference Paper

Abnormal Occupancy Grid Map Recognition using Attention Network

Fuqin Deng
Hua Feng
Mingjian Liang
Qi Feng
Ningbo Yi
Yong Yang
Yuan Gao 0024
Junfeng Chen

The occupancy grid map is a critical component of autonomous positioning and navigation in the mobile robotic system, as many other systems' performance depends heavily on it. To guarantee the quality of the occupancy grid maps, researchers previously had to perform tedious manual recognition for a long time. This work focuses on automatic abnormal occupancy grid map recognition using the residual neural network with novel attention mechanism modules. We propose an effective channel and spatial Residual Squeeze-and-Excitation (csRSE) attention module, which contains a residual block for producing hierarchical features, followed by both channel SE (cSE) block and spatial SE (sSE) block for the sufficient information extraction along the channel and spatial pathways. To further summarize the occupancy grid map characteristics and experiments with our csRSE attention modules, we constructed a dataset called occupancy grid map dataset (OGMD) for our experiments. On this OGMD test dataset, we tested a few variants of our proposed structure and compared them with other attention mechanisms. Our experimental results show that the proposed attention network can infer the abnormal map with state-of-the-art (SOTA) accuracy of 96. 23% for abnormal occupancy grid map recognition.

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NeurIPS Conference 2022 Conference Paper

Untargeted Backdoor Watermark: Towards Harmless and Stealthy Dataset Copyright Protection

Yiming Li
Yang Bai
Yong Jiang
Yong Yang
Shu-Tao Xia
Bo Li

Deep neural networks (DNNs) have demonstrated their superiority in practice. Arguably, the rapid development of DNNs is largely benefited from high-quality (open-sourced) datasets, based on which researchers and developers can easily evaluate and improve their learning methods. Since the data collection is usually time-consuming or even expensive, how to protect their copyrights is of great significance and worth further exploration. In this paper, we revisit dataset ownership verification. We find that existing verification methods introduced new security risks in DNNs trained on the protected dataset, due to the targeted nature of poison-only backdoor watermarks. To alleviate this problem, in this work, we explore the untargeted backdoor watermarking scheme, where the abnormal model behaviors are not deterministic. Specifically, we introduce two dispersibilities and prove their correlation, based on which we design the untargeted backdoor watermark under both poisoned-label and clean-label settings. We also discuss how to use the proposed untargeted backdoor watermark for dataset ownership verification. Experiments on benchmark datasets verify the effectiveness of our methods and their resistance to existing backdoor defenses.

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IROS Conference 2021 Conference Paper

FEANet: Feature-Enhanced Attention Network for RGB-Thermal Real-time Semantic Segmentation

Fuqin Deng
Hua Feng
Mingjian Liang
Hongmin Wang
Yong Yang
Yuan Gao 0024
Junfeng Chen
Junjie Hu 0003

The RGB-Thermal (RGB-T) information for semantic segmentation has been extensively explored in recent years. However, most existing RGB-T semantic segmentation usually compromises spatial resolution to achieve real-time inference speed, which leads to poor performance. To better extract detail spatial information, we propose a two-stage Feature-Enhanced Attention Network (FEANet) for the RGB-T semantic segmentation task. Specifically, we introduce a Feature-Enhanced Attention Module (FEAM) to excavate and enhance multi-level features from both the channel and spatial views. Benefited from the proposed FEAM module, our FEANet can preserve the spatial information and shift more attention to high-resolution features from the fused RGB-T images. Extensive experiments on the urban scene dataset demonstrate that our FEANet outperforms other state-of-the-art (SOTA) RGB-T methods in terms of objective metrics and subjective visual comparison (+2. 6% in global mAcc and +0. 8% in global mIoU). For the 480 × 640 RGB-T test images, our FEANet can run with a real-time speed on an NVIDIA GeForce RTX 2080 Ti card.

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JBHI Journal 2019 Journal Article

Deception Decreases Brain Complexity

Junfeng Gao
Jian Song
Yong Yang
Shun Yao
Jinan Guan
Huifang Si
Hui Zhou
Sheng Ge

Extensive evidence suggests the feasibility of lie detection using electroencephalograms (EEGs). However, it is largely unknown whether there are any differences in the nonlinear features of EEGs between guilty and innocent subjects. In this study, we proposed a complexity-based method to distinguish lying from truth telling. A total of 35 participants were randomly divided into two groups, and their EEG signals were recorded with 14 electrodes. Averages for sequential sets of five trials were first calculated for the probe responses within each subject. Next, a common wavelet entropy (WE) measure and an improved one were used to quantify complexity from each five-trial average. The results show that for both measures, the WE values in the guilty subjects are statistically lower than those in the innocent subjects for most of the 14 electrodes. More importantly, using the improved measure, the difference in WE between the two groups of subjects significantly increases for 11 brain regions compared with the values from the common measure. Finally, the highest balanced classification accuracy, 89. 64%, is achieved when using the combined WE feature vector in five brain regions from the sites of Pz, P3, C4, Cz, and C3. Our findings indicate that the lying task elicits a more ordered brain activity in some specific brain regions than the task of telling the truth. This study not only demonstrates that improved WE measurements could be a powerful quantitative index for detecting lying but also sheds light on the brain mechanisms underlying deceptive behaviors.

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JBHI Journal 2019 Journal Article

Multimodal Medical Image Fusion Based on Fuzzy Discrimination With Structural Patch Decomposition

Yong Yang
Jiahua Wu
Shuying Huang
Yuming Fang
Pan Lin
Yue Que

Multimodal medical image fusion, emerging as a hot topic, aims to fuse images with complementary multi-source information. In this paper, we propose a novel multimodal medical image fusion method based on structural patch decomposition (SPD) and fuzzy logic technology. First, the SPD method is employed to extract two salient features for fusion discrimination. Next, two novel fusion decision maps called an incomplete fusion map and supplemental fusion map are constructed from salient features. In this step, the supplemental map is constructed by our defined two different fuzzy logic systems. The supplemental and incomplete maps are then combined to construct an initial fusion map. The final fusion map is obtained by processing the initial fusion map with a Gaussian filter. Finally, a weighted average approach is adopted to create the final fused image. Additionally, an effective color medical image fusion scheme that can effectively prevent color distortion and obtain superior diagnostic effects is also proposed to enhance fused images. Experimental results clearly demonstrate that the proposed method outperforms state-of-theart methods in terms of subjective visual and quantitative evaluations.

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JBHI Journal 2017 Journal Article

The Reorganization of Human Brain Networks Modulated by Driving Mental Fatigue

Chunlin Zhao
Min Zhao
Yong Yang
Junfeng Gao
Nini Rao
Pan Lin

The organization of the brain functional network is associated with mental fatigue, but little is known about the brain network topology that is modulated by the mental fatigue. In this study, we used the graph theory approach to investigate reconfiguration changes in functional networks of different electroen-cephalography (EEG) bands from 16 subjects performing a simulated driving task. Behavior and brain functional networks were compared between the normal and driving mental fatigue states. The scores of subjective self-reports indicated that 90 min of simulated driving-induced mental fatigue. We observed that coherence was significantly increased in the frontal, central, and temporal brain regions. Furthermore, in the brain network topology metric, significant increases were observed in the clustering coefficient (Cp) for beta, alpha, and delta bands and the character path length (Lp) for all EEG bands. The normalized measures γ showed significant increases in beta, alpha, and delta bands, and λ showed similar patterns in beta and theta bands. These results indicate that functional network topology can shift the network topology structure toward a more economic but less efficient configuration, which suggests low wiring costs in functional networks and disruption of the effective interactions between and across cortical regions during mental fatigue states. Graph theory analysis might be a useful tool for further understanding the neural mechanisms of driving mental fatigue.

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YNIMG Journal 2016 Journal Article

Identifying functional subdivisions in the human brain using meta-analytic activation modeling-based parcellation

Yong Yang
Lingzhong Fan
Congying Chu
Junjie Zhuo
Jiaojian Wang
Peter T. Fox
Simon B. Eickhoff
Tianzi Jiang

Parcellation of the human brain into fine-grained units by grouping voxels into distinct clusters has been an effective approach for delineating specific brain regions and their subregions. Published neuroimaging studies employing coordinate-based meta-analyses have shown that the activation foci and their corresponding behavioral categories may contain useful information about the anatomical–functional organization of brain regions. Inspired by these developments, we proposed a new parcellation scheme called meta-analytic activation modeling-based parcellation (MAMP) that uses meta-analytically obtained information. The raw meta data, including the experiments and the reported activation coordinates related to a brain region of interest, were acquired from the Brainmap database. Using this data, we first obtained the “modeled activation” pattern by modeling the voxel-wise activation probability given spatial uncertainty for each experiment that featured at least one focus within the region of interest. Then, we processed these “modeled activation” patterns across the experiments with a K-means clustering algorithm to group the voxels into different subregions. In order to verify the reliability of the method, we employed our method to parcellate the amygdala and the left Brodmann area 44 (BA44). The parcellation results were quite consistent with previous cytoarchitectonic and in vivo neuroimaging findings. Therefore, the MAMP proposed in the current study could be a useful complement to other methods for uncovering the functional organization of the human brain.

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YNIMG Journal 2015 Journal Article

Co-activation Probability Estimation (CoPE): An approach for modeling functional co-activation architecture based on neuroimaging coordinates

Congying Chu
Lingzhong Fan
Claudia R. Eickhoff
Yong Liu
Yong Yang
Simon B. Eickhoff
Tianzi Jiang

Recent progress in functional neuroimaging has prompted studies of brain activation during various cognitive tasks. Coordinate-based meta-analysis has been utilized to discover the brain regions that are consistently activated across experiments. However, within-experiment co-activation relationships, which can reflect the underlying functional relationships between different brain regions, have not been widely studied. In particular, voxel-wise co-activation, which may be able to provide a detailed configuration of the co-activation network, still needs to be modeled. To estimate the voxel-wise co-activation pattern and deduce the co-activation network, a Co-activation Probability Estimation (CoPE) method was proposed to model within-experiment activations for the purpose of defining the co-activations. A permutation test was adopted as a significance test. Moreover, the co-activations were automatically separated into local and long-range ones, based on distance. The two types of co-activations describe distinct features: the first reflects convergent activations; the second represents co-activations between different brain regions. The validation of CoPE was based on five simulation tests and one real dataset derived from studies of working memory. Both the simulated and the real data demonstrated that CoPE was not only able to find local convergence but also significant long-range co-activation. In particular, CoPE was able to identify a ‘core’ co-activation network in the working memory dataset. As a data-driven method, the CoPE method can be used to mine underlying co-activation relationships across experiments in future studies.

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IROS Conference 2012 Conference Paper

A novel design of Tri-star wheeled mobile robot for high obstacle climbing

Yong Yang
Huihuan Qian
Xinyu Wu 0001
Guiyun Xu
Yangsheng Xu

This paper proposed a novel Tri-star wheeled robot called “Tribot”, which targets on high obstacle performance in unstructured environments, especially at the performance for climbing vertical obstacles. Tribot equips with six Tri-star wheels and each wheel can be driven independently. The chassis of the Tribot is divided into two parts which are connected by an articulated mechanism, making the Tribot has a remarkable obstacle performance to adapt changing environments mechanically, without any interpolate complex control. Numerous experiments have been conducted for vertical obstacle performance tests. Although the diameter of the wheel of the Tribot is only 220 mm, the robot can climb over vertical obstacle of 450 mm high, twice more of the wheel diameter. All results show that Tribot has excellent vertical climbing performance in unstructured environments.

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ICRA Conference 2001 Conference Paper

An Efficient Scanning Pattern for Layered Manufacturing Processes

Yong Yang
Jerry Y. H. Fuh
Han Tong Loh
Yun Gan Wang

Path generation is an important factor that affects the quality and efficiency of most layered manufacturing processes such as SLS, SLA and FDM. This paper introduces an efficient path generation algorithm. The principle of the algorithm and the implementation are presented. A comparative study is used to analyze the effectiveness of this method. The results of comparison on both the path length and processing time between the traditional method and the proposed method are discussed.

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