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

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

Outcomes From the Multicenter ACCRU-LY-1804/CARiBOU TRIAL (Cytarabine, Acalabrutinib and Rituximab Integrated With Bortezomib-Based Outpatient Therapy) in 1st Line Mantle Cell Lymphoma.

American journal of hematology·2026
Same author

Real-world evidence for pembrolizumab gemcitabine vinorelbine and liposomal doxorubicin in classical Hodgkin lymphoma.

Blood advances·2026
Same author

Exploring the interaction of APOE-ε4and PICALM rs3851179 with dynamic functional connectivity in healthy middle-aged adults at risk for Alzheimer's disease.

Journal of neural engineering·2026
Same author

Fractal Dimension of Resting-State EEG as a Biomarker for Autonomous Sensory Meridian Response (ASMR).

IEEE journal of biomedical and health informatics·2025
Same author

A Novel Approach for the Early Identification of Genetic Risk Factors for Alzheimer's Disease Using EEG and Psychometric Data.

IEEE journal of biomedical and health informatics·2025
Same author

Barriers to Investigator-Initiated Clinical Trial Enrollment in Frontline Large B-Cell Lymphoma.

Clinical lymphoma, myeloma & leukemia·2025

相关实验视频

Updated: Jan 9, 2026

Brain Imaging Investigation of the Neural Correlates of Emotion Regulation
14:04

Brain Imaging Investigation of the Neural Correlates of Emotion Regulation

Published on: August 26, 2011

13.0K

使用机器学习在情绪调节期间模拟EEG衍生的大脑活动.

Mahdis Hojjati, Shyamal Y Dharia, Sergio G Camorlinga

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    概括

    这项研究使用脑电图 (EEG) 和机器学习来预测情绪调节 (ER) 的成功. 机器学习模型准确地识别了与成功的ER相关的大脑活动模式,突出了额头区域和β频率.

    更多相关视频

    Brain Imaging Investigation of the Memory-Enhancing Effect of Emotion
    15:57

    Brain Imaging Investigation of the Memory-Enhancing Effect of Emotion

    Published on: May 4, 2011

    17.2K
    Psychophysiological Assessment of the Effectiveness of Emotion Regulation Strategies in Childhood
    08:09

    Psychophysiological Assessment of the Effectiveness of Emotion Regulation Strategies in Childhood

    Published on: February 11, 2017

    12.0K

    相关实验视频

    Last Updated: Jan 9, 2026

    Brain Imaging Investigation of the Neural Correlates of Emotion Regulation
    14:04

    Brain Imaging Investigation of the Neural Correlates of Emotion Regulation

    Published on: August 26, 2011

    13.0K
    Brain Imaging Investigation of the Memory-Enhancing Effect of Emotion
    15:57

    Brain Imaging Investigation of the Memory-Enhancing Effect of Emotion

    Published on: May 4, 2011

    17.2K
    Psychophysiological Assessment of the Effectiveness of Emotion Regulation Strategies in Childhood
    08:09

    Psychophysiological Assessment of the Effectiveness of Emotion Regulation Strategies in Childhood

    Published on: February 11, 2017

    12.0K

    科学领域:

    • 神经科学是一个神经科学.
    • 认知科学 认知科学
    • 计算神经科学是一种神经科学.

    背景情况:

    • 情绪调节 (ER) 对心理健康和社会功能至关重要,特别是在压力下.
    • 了解ER的神经机制对于开发有效的干预措施至关重要.
    • 评估ER的现有方法可能是主观的;需要客观的措施.

    研究的目的:

    • 通过使用电脑电图 (EEG) 来调查情绪调节期间的大脑活动模式.
    • 开发和验证机器学习 (ML) 模型,用于预测成功与不成功的ER.
    • 确定与ER成功相关的特定EEG信号特征 (时间和频率域).

    主要方法:

    • 参与者观看了情感/中性图像,并被告知要么正常观看,要么调节情绪.
    • 在时间 (全球场功率) 和频率 (功率光谱密度) 领域收集和分析了EEG数据.
    • 机器学习模型,包括带有最大平均差异 (MMD) 损失的神经网络,在EEG特征上进行训练,以预测ER成功.

    主要成果:

    • 全球场功率 (GFP) 在前脑和中脑区域中发现了显著的差异.
    • 确定了theta,beta和gamma频段对ER很重要.
    • 一个独立于主体的神经网络实现了75.57%的F1得分宏来预测ER成功,强调额头区域和β频率.

    结论:

    • 将EEG数据与先进的ML数据相结合,为评估ER提供了一个准确,客观的框架.
    • 前脑区域和β频率信号是情绪调节水平的关键预测因素.
    • 这种基于EEG的方法为ER评估和个性化心理健康治疗提供了一种新方法.