Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation06:09

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

931
This article presents a method for estimating same-day P300 speller Brain-Computer Interface (BCI) accuracy using a small testing dataset.
931
Modeling the Functional Network for Spatial Navigation in the Human Brain05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

1.5K
This paper presents an integrative approach to investigating the functional network for spatial navigation in the human brain. This approach incorporates a large-scale neuroimaging meta-analytic database, resting-state functional magnetic resonance imaging, and network modeling and graph-theoretical techniques.
1.5K
Natural Selection and Adaptation01:15

Natural Selection and Adaptation

1.2K
Natural selection, a fundamental concept in evolutionary biology, is the mechanism by which evolution is driven, favoring organisms that are best adapted to their environments. This process enhances their chances of survival and reproduction. Adaptation, a key outcome of this process, involves genetic modifications that optimize an organism's functionality under specific environmental challenges, such as extreme cold or thinner air at high altitudes.
Beyond physical adaptations,...
1.2K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

8.0K
Existing algorithms generate one solution for a biomarker detection dataset. This protocol demonstrates the existence of multiple similarly effective solutions and presents a user-friendly software to help biomedical researchers investigate their datasets for the proposed challenge. Computer scientists may also provide this feature in their biomarker detection...
8.0K
Using an EEG-Based Brain-Computer Interface for Virtual Cursor Movement with BCI200012:07

Using an EEG-Based Brain-Computer Interface for Virtual Cursor Movement with BCI2000

18.4K
In this video, we demonstrate the steps required to run a brain-computer interface experiment, including setting up the EEG cap, calibrating the system, and training the user to move a cursor in two dimensions using imagined...
18.4K
Three-Dimensional Shape Modeling and Analysis of Brain Structures05:33

Three-Dimensional Shape Modeling and Analysis of Brain Structures

7.6K
We introduce a semi-automatic protocol for shape analysis on brain structures, including image segmentation using open software, and further group-wise shape analysis using an automated modeling package. Here, we demonstrate each step of the 3D shape analysis protocol with hippocampal segmentation from brain MR...
7.6K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Bayesian Inference on Brain-Computer Interfaces via GLASS.

Journal of the American Statistical Association·2025
Same author

In Vitro Modeling of Natural Killer Cell Cytotoxicity to Inform Personalized ALS Therapeutics.

Annals of clinical and translational neurology·2025
Same author

Early immune system changes in amyotrophic lateral sclerosis correlate with later disease progression.

Med (New York, N.Y.)·2025
Same author

BAYESIAN LEARNING OF COVID-19 VACCINE SAFETY WHILE INCORPORATING ADVERSE EVENTS ONTOLOGY.

The annals of applied statistics·2024
Same author

Peripheral Immune Profiles Predict ALS Progression in an Age- and Sex-Dependent Manner.

Neurology(R) neuroimmunology & neuroinflammation·2024
Same author

Mining adverse events in large frequency tables with ontology, with an application to the vaccine adverse event reporting system.

Statistics in medicine·2023
Same journal

Probabilistic Joint and Individual Variation Explained (ProJIVE) for Data Integration.

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America·2026
Same journal

fastkqr: A Fast Algorithm for Kernel Quantile Regression.

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America·2026
Same journal

Empirical Bayes Covariance Decomposition, and a Solution to the Multiple Tuning Problem in Sparse PCA.

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America·2026
Same journal

Joint Registration and Conformal Prediction for Partially Observed Functional Data.

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America·2026
Same journal

Efficient Decision Trees for Tensor Regressions.

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America·2026
Same journal

Distributed Nonparametric Regression with Heterogeneity Through Prediction-Based Aggregation.

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America·2026
查看所有相关文章

相关实验视频

Updated: Jan 20, 2026

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
06:09

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Published on: September 8, 2023

931

使用二进制条件自行回归模型进行空间自适应选择,并应用于脑-计算机接口.

Zikai Lin, Junsouk Choi, Ruoxuan Mao

    Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
    |January 19, 2026
    PubMed
    概括
    此摘要是机器生成的。

    这项研究引入了新的贝叶斯模型用于医学成像分析,提高了有限数据的预测准确性. 使用二进制条件自行回归模型 (SAS-BCAR) 的空间自适应选择增强了复杂成像数据集的特征选择.

    关键词:
    贝叶斯的方法 贝叶斯的方法二元条件自回归模型二元条件自回归模型大脑 - 计算机接口图像上的标量回归.

    更多相关视频

    Modeling the Functional Network for Spatial Navigation in the Human Brain
    05:55

    Modeling the Functional Network for Spatial Navigation in the Human Brain

    Published on: October 13, 2023

    1.5K
    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
    07:35

    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

    Published on: October 11, 2018

    8.0K

    相关实验视频

    Last Updated: Jan 20, 2026

    P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
    06:09

    P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

    Published on: September 8, 2023

    931
    Modeling the Functional Network for Spatial Navigation in the Human Brain
    05:55

    Modeling the Functional Network for Spatial Navigation in the Human Brain

    Published on: October 13, 2023

    1.5K
    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
    07:35

    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

    Published on: October 11, 2018

    8.0K

    科学领域:

    • 医学成像分析 医学成像分析
    • 统计建模 统计建模
    • 机器学习 机器学习

    背景情况:

    • 图像上的标量回归在医学成像中面临着挑战,因为样本大小小小和高维数据.
    • 图像预测器经常显示空间异质的模式和与响应变量的非线性关系.

    研究的目的:

    • 提出一个新的贝叶斯标量对图像回归模型,以改进医学成像数据的分析.
    • 为了应对有限的样本大小,高维度,空间异质性和非线性关联的挑战.

    主要方法:

    • 介绍了贝叶斯模型与空间自适应选择使用二进制条件自回归模型 (SAS-BCAR) 之前.
    • 利用二进制条件自回归模型来捕捉特征选择指标中的空间依赖性.
    • 整合了适应性特征选择机制,以便在图像区域之间进行精确和强大的选择.

    主要成果:

    • 与现有方法相比,SAS-BCAR模型在模拟中表现出优异的预测性能.
    • 该模型在训练数据有限的场景中表现出色.
    • 展示了空间结构稀疏性模式的有效识别和处理非线性关系.

    结论:

    • 拟议的SAS-BCAR模型为医学成像中的标量对图像回归提供了一种强大而准确的方法.
    • 它提供了显著的优势,特别是在处理有限的数据和复杂的空间依赖时.
    • 该模型对使用电脑脑学数据的脑电脑接口等应用非常有希望.