https://yuzhe-liu-lab.github.io/resources/Resources | 开源资源Hugo Blox Builder (https://hugoblox.com)en-usSun, 24 May 2026 00:00:00 +0000https://yuzhe-liu-lab.github.io/media/icon_hu12240421747060588630.pnghttps://yuzhe-liu-lab.github.io/resources/https://yuzhe-liu-lab.github.io/resources/brain-rbf/Sun, 24 May 2026 00:00:00 +0000https://yuzhe-liu-lab.github.io/resources/brain-rbf/<p><a href="https://github.com/Yuzhe-Liu-Lab/Brain-RBF" target="_blank" rel="noopener">GitHub Repository</a></p> <h2 id="related-publications">Related Publications</h2> <ul> <li><a href="https://yuzhe-liu-lab.github.io/publication/2026-yihan-automorph/">Quantifying Morphology-Related Deviations in Brain Strain Using an Automated Mesh Morphing Method</a></li> </ul> <h2 id="introduction">Introduction</h2> <p>Brain-RBF provides a workflow for morphing a baseline finite element head model to match target brain morphology using radial basis function thin-plate spline (RBF-TPS) interpolation. The repository is designed around node-coordinate input from the THUMS head model, allowing users to extract nodal coordinates from the model keyword file and generate morphology-adapted brain meshes for downstream finite element analysis.</p> <h2 id="中文介绍">中文介绍</h2> <p>Brain-RBF 提供了一套基于径向基函数薄板样条（RBF-TPS）的有限元头部模型网格变形流程，可将基准模型变形到目标脑形态。该仓库以 THUMS 头部模型的节点坐标为主要输入，用户可从模型 K 文件中的节点信息提取坐标，并生成与个体脑形态相匹配的模型，用于后续有限元仿真与脑应变分析。</p>https://yuzhe-liu-lab.github.io/resources/clustering-template-brain-strain/Sun, 24 May 2026 00:00:00 +0000https://yuzhe-liu-lab.github.io/resources/clustering-template-brain-strain/<p><a href="https://github.com/Yuzhe-Liu-Lab/Clustering-Template-for-Brain-Strain" target="_blank" rel="noopener">GitHub Repository</a></p> <h2 id="related-publications">Related Publications</h2> <ul> <li>Related publications will be added as they become available.</li> </ul> <h2 id="introduction">Introduction</h2> <p>Clustering Template for Brain Strain provides public templates and supporting resources for organizing brain-strain simulation outputs and preparing them for clustering-based analysis. It is intended to make repeated analysis of strain patterns more consistent across simulation cases and easier to integrate into group-level workflows.</p> <h2 id="中文介绍">中文介绍</h2> <p>Clustering Template for Brain Strain 提供用于整理脑应变仿真结果并开展聚类分析的公开模板和辅助资源。该仓库旨在提高不同仿真案例中脑应变模式分析的一致性，便于将多案例结果整合到组学或队列层面的分析流程中。</p>https://yuzhe-liu-lab.github.io/resources/deep-learning-brain-model/Sun, 24 May 2026 00:00:00 +0000https://yuzhe-liu-lab.github.io/resources/deep-learning-brain-model/<h2 id="introduction">Introduction</h2> <p>The Deep Learning Brain Model estimates the spatiotemporal distribution of brain strain from six-axis head kinematics measured by wearable devices such as instrumented mouthguards. By using linear acceleration and angular velocity as inputs, the model provides rapid brain-strain calculation for head-impact events and supports downstream analysis of traumatic brain injury risk.</p> <p>If you are interested in using the model or collaborating with us, please contact us.</p> <h2 id="中文介绍">中文介绍</h2> <p>深度学习大脑模型可根据智能牙套等可穿戴设备采集到的头部六轴运动，快速计算头部碰撞过程中大脑应变的时空分布。该模型以头部线加速度和角速度为输入，输出大脑组织层面的应变响应，可用于创伤性脑损伤风险评估和头部冲击数据分析。</p> <p>如需使用该模型或开展合作，请联系我们。</p> <h2 id="related-publications">Related Publications</h2> <ul> <li><a href="https://yuzhe-liu-lab.github.io/publication/2021-mlhm/">Rapid Estimation of Entire Brain Strain Using Deep Learning Models</a></li> <li><a href="https://yuzhe-liu-lab.github.io/publication/2022-pca/">Finding the Spatial Co-Variation of Brain Deformation With Principal Component Analysis</a></li> <li><a href="https://yuzhe-liu-lab.github.io/publication/2024-mlhmtwo/">Brain Deformation Estimation With Transfer Learning for Head Impact Datasets Across Impact Types</a></li> <li><a href="https://yuzhe-liu-lab.github.io/publication/2024-mlhmthree/">Adaptive Machine Learning Head Model Across Different Head Impact Types Using Unsupervised Domain Adaptation and Generative Adversarial Networks</a></li> <li><a href="https://yuzhe-liu-lab.github.io/publication/2024-denoise/">AI-Based Denoising of Head Impact Kinematics Measurements With Convolutional Neural Network for Traumatic Brain Injury Prediction</a></li> </ul>https://yuzhe-liu-lab.github.io/resources/fe-head-impact-simulations/Sun, 24 May 2026 00:00:00 +0000https://yuzhe-liu-lab.github.io/resources/fe-head-impact-simulations/<p><a href="https://github.com/Yuzhe-Liu-Lab/FE_head_impact_simulations_Public" target="_blank" rel="noopener">GitHub Repository</a></p> <h2 id="related-publications">Related Publications</h2> <ul> <li><a href="https://yuzhe-liu-lab.github.io/publication/2025-inertialforce/">Local and Global Effects of Inertial Force Components Producing Brain Strain During Head Impacts</a></li> <li><a href="https://yuzhe-liu-lab.github.io/publication/2021-mlhm/">Rapid Estimation of Entire Brain Strain Using Deep Learning Models</a></li> <li><a href="https://yuzhe-liu-lab.github.io/publication/2021-relationship/">The Relationship Between Brain Injury Criteria and Brain Strain Across Different Types of Head Impacts Can Be Different</a></li> <li><a href="https://yuzhe-liu-lab.github.io/publication/2021-predicitive/">Predictive Factors of Kinematics in Traumatic Brain Injury from Head Impacts Based on Statistical Interpretation</a></li> <li><a href="https://yuzhe-liu-lab.github.io/publication/2022-pca/">Finding the Spatial Co-Variation of Brain Deformation With Principal Component Analysis</a></li> <li><a href="https://yuzhe-liu-lab.github.io/publication/2022-piecewise/">Piecewise Multivariate Linearity Between Kinematic Features and Cumulative Strain Damage Measure (CSDM) Across Different Types of Head Impacts</a></li> <li><a href="https://yuzhe-liu-lab.github.io/publication/2022-timewindows/">Time Window of Head Impact Kinematics Measurement for Calculation of Brain Strain and Strain Rate in American Football</a></li> <li><a href="https://yuzhe-liu-lab.github.io/publication/2023-brain-strain-rate-ambiguity/">Brain strain rate response: Addressing computational ambiguity and experimental data for model validation</a></li> <li><a href="https://yuzhe-liu-lab.github.io/publication/2024-mpsrcal/">Differences between two maximal principal strain rate calculation schemes in traumatic brain analysis with in-vivo and in-silico datasets</a></li> <li><a href="https://yuzhe-liu-lab.github.io/publication/2024-mlhmtwo/">Brain Deformation Estimation With Transfer Learning for Head Impact Datasets Across Impact Types</a></li> <li><a href="https://yuzhe-liu-lab.github.io/publication/2024-mlhmthree/">Adaptive Machine Learning Head Model Across Different Head Impact Types Using Unsupervised Domain Adaptation and Generative Adversarial Networks</a></li> <li><a href="https://yuzhe-liu-lab.github.io/publication/2024-denoise/">AI-Based Denoising of Head Impact Kinematics Measurements With Convolutional Neural Network for Traumatic Brain Injury Prediction</a></li> <li><a href="https://yuzhe-liu-lab.github.io/publication/2023-classification/">Machine-learning-based head impact subtyping based on the spectral densities of the measurable head kinematics</a></li> </ul> <h2 id="introduction">Introduction</h2> <p>FE Head Impact Simulations contains MATLAB code for finite element simulations of head impacts. The repository supports modeling the dynamic biomechanical behavior of the human head during impact and analyzing the resulting stress and strain distributions, helping users reproduce and extend head-injury biomechanics workflows.</p> <h2 id="中文介绍">中文介绍</h2> <p>FE Head Impact Simulations 收录了用于头部冲击有限元仿真的 MATLAB 代码。该仓库可用于模拟人体头部在动态冲击过程中的生物力学响应，并分析相应的应力、应变分布，便于复现和扩展创伤性脑损伤相关的生物力学分析流程。</p>https://yuzhe-liu-lab.github.io/resources/instrumented-mouthguard/Sun, 24 May 2026 00:00:00 +0000https://yuzhe-liu-lab.github.io/resources/instrumented-mouthguard/<h2 id="introduction">Introduction</h2> <p>The instrumented mouthguard is a custom-fit smart device for measuring six-axis head kinematics during head impacts. Through a customized assembly interface, the device is tightly coupled to the upper dentition to support accurate measurement of head linear acceleration and angular velocity. High-bandwidth inertial sensors are embedded in the mouthguard to capture impact kinematics, while Bluetooth enables wireless data transfer and wireless charging supports convenient repeated use.</p> <p>If you are interested in using or collaborating on the instrumented mouthguard, please contact us.</p> <h2 id="collaborator">Collaborator</h2> <p>Associate Professor Li Wang, Beihang University.</p> <h2 id="中文介绍">中文介绍</h2> <p>智能牙套是一种用于头部碰撞六轴运动精确测量的定制化可穿戴设备。通过定制的装配界面，牙套可与上颌牙列稳定耦合，从而更准确地测量头部线加速度和角速度。设备内部集成高带宽惯性传感器，用于采集头部碰撞过程中的运动学数据，并通过蓝牙实现无线数据传输，同时支持无线充电，便于重复使用和实验部署。</p> <p>如需使用该设备或开展合作，请联系我们。</p> <h2 id="合作者">合作者</h2> <p>北京航空航天大学 王立副教授。</p> <h2 id="related-publications">Related Publications</h2> <ul> <li><a href="https://yuzhe-liu-lab.github.io/publication/2020-mg/">Validation and Comparison of Instrumented Mouthguards for Measuring Head Kinematics and Assessing Brain Deformation in Football Impacts</a></li> <li><a href="https://yuzhe-liu-lab.github.io/publication/2021-highschool/">Identifying Factors Associated with Head Impact Kinematics and Brain Strain in High School American Football via Instrumented Mouthguards</a></li> <li><a href="https://yuzhe-liu-lab.github.io/publication/2022-timewindows/">Time Window of Head Impact Kinematics Measurement for Calculation of Brain Strain and Strain Rate in American Football</a></li> <li><a href="https://yuzhe-liu-lab.github.io/publication/2024-denoise/">AI-Based Denoising of Head Impact Kinematics Measurements With Convolutional Neural Network for Traumatic Brain Injury Prediction</a></li> </ul>