Jove
Visualize
联系我们

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

Long-range correlations in alpha-band of electroencephalogram: a nonlinear embedding and detrended fluctuation analysis.

Frontiers in neuroinformatics·2026
Same author

Authors' Response, "Reply to the comments on Reporting guidelines for Case Reports In Mental health and Psychiatry (CRIMP)".

Indian journal of psychiatry·2026
Same author

Development and psychometric evaluation of an abbreviated form of the revised suicide crisis inventory (A-SCI)-2 in major depression: A multicentric investigation.

Asian journal of psychiatry·2026
Same author

On-device single channel EEG classification on Android smartphones using lightweight machine learning models.

Biomedical physics & engineering express·2026
Same author

Reporting guidelines for Case Reports In Mental health and Psychiatry (CRIMP).

Indian journal of psychiatry·2025
Same author

Development of a Minimum Data Set (MDS) for the National Suicide Registry, India.

Cureus·2025
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

相关实验视频

Updated: Jan 9, 2026

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare
06:34

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

Published on: July 7, 2023

3.1K

在Android设备上使用自然环境机器学习进行眼睛状态预测 脑电图应用程序

Doli Hazarika, Sanjay Chhaba, Ramdas Ransing

    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

    我们创建了一个轻量级的机器学习管道,用于Android设备上的脑电图 (EEG) 分类. 该系统在分类眼睛状态方面达到90%的准确性,即使数据有限.

    科学领域:

    • 神经科学是一个神经科学.
    • 机器学习 机器学习
    • 移动健康服务提供者

    背景情况:

    • 脑电图 (EEG) 信号对于在医学和认知上理解大脑活动至关重要.
    • 对于EEG分析的深度学习模型需要大量的数据和计算资源,这限制了它们在现实世界中的应用.
    • 需要在资源有限的设备上部署高效的EEG分析方法.

    研究的目的:

    • 开发一个轻量级的机器学习管道用于安卓设备上的EEG分类.
    • 使用TensorFlow Lite优化对有限数据场景的EEG分类.
    • 通过对眼睛状态分类的案例研究来证明管道的有效性.

    主要方法:

    • 使用TensorFlow Lite开发了一个机器学习管道,用于Android部署.
    • 收集了十名参与者的EEG数据,使用CameraEEG应用程序对眼睛状态进行分类 (眼睛打开与眼睛关闭).
    • 应用文物去除 (嵌入式ASR) 和功率光谱特征提取,然后训练单通道支向量机 (SVM) 模型.

    主要成果:

    • 该SVM模型在分类眼睛状态方面实现了90%的准确性.
    • 该模型在各种评估指标中显示出强度.
    • 安卓应用程序在智能手机 (谷歌Pixel 7 Pro,三星S22) 上成功执行了EEG分类.

    更多相关视频

    Using Electroencephalography Measurements and High-quality Video Recording for Analyzing Visual Perception of Media Content
    10:41

    Using Electroencephalography Measurements and High-quality Video Recording for Analyzing Visual Perception of Media Content

    Published on: May 26, 2018

    7.3K
    Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
    06:37

    Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

    Published on: December 15, 2023

    5.2K

    相关实验视频

    Last Updated: Jan 9, 2026

    A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare
    06:34

    A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

    Published on: July 7, 2023

    3.1K
    Using Electroencephalography Measurements and High-quality Video Recording for Analyzing Visual Perception of Media Content
    10:41

    Using Electroencephalography Measurements and High-quality Video Recording for Analyzing Visual Perception of Media Content

    Published on: May 26, 2018

    7.3K
    Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
    06:37

    Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

    Published on: December 15, 2023

    5.2K

    结论:

    • 开发的轻量级管道允许在移动设备上进行高效的EEG分类,即使数据有限.
    • 这种方法可以在自然环境中进行生理测量.
    • 潜在的应用包括认知工作量监测,发作检测和心理健康评估.