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

Toward Multi-Dimensional Depression Assessment: EEG-Based Machine Learning and Neurophysiological Interpretation for Diagnosis, Severity, and Cognitive Decline.

Brain sciences·2026
Same author

A Hybrid CNN-SVM Approach for ECG-Based Multi-Class Differential Diagnosis of PTSD, Depression, and Panic Attack.

Biosensors·2026
Same author

Cognitive load in cyclists while navigating in traffic: Effects of static and dynamic route events on neural activity of cyclists measured by fNIRS.

PloS one·2025
Same author

Diffusion Model-Based Augmentation Using Asymmetric Attention Mechanisms for Cardiac MRI Images.

Diagnostics (Basel, Switzerland)·2025
Same author

SHAP-Based Identification of Potential Acoustic Biomarkers in Patients with Post-Thyroidectomy Voice Disorder.

Diagnostics (Basel, Switzerland)·2025
Same author

ECG Signal Analysis for Detection and Diagnosis of Post-Traumatic Stress Disorder: Leveraging Deep Learning and Machine Learning Techniques.

Diagnostics (Basel, Switzerland)·2025

相关实验视频

Updated: Jan 13, 2026

Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders
05:49

Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders

Published on: November 1, 2024

1.2K

可解释的AI对疼痛感知:使用DeepSHAP和CNNs进行主体独立的EEG解码

Feyzi Alkım Aktaş1, Aykut Eken2, Osman Erogul3

  • 1TOBB University of Economics and Technology, Söğütözü, Söğütözü Cd. No:43, 06510 Çankaya/Ankara, Ankara, 06560, TURKEY.

Biomedical physics & engineering express
|January 7, 2026
PubMed
概括

这项研究使用可解释的深度学习来从脑电图 (EEG) 信号中解码疼痛水平,准确度为95.85%. 该方法识别了与不同疼痛强度相关的特定脑波模式,使客观的疼痛监测成为可能.

关键词:
这就是BCI的意义.深度学习 (Deep Learning) 是一种深度学习.这是一个EEGEEGEEGEEGEEG.可解释的人工智能迷失了 迷失了机器学习 机器学习解码疼痛的解码方法

更多相关视频

Monitoring Acupuncture Effects on Human Brain by fMRI
09:55

Monitoring Acupuncture Effects on Human Brain by fMRI

Published on: April 8, 2010

15.9K
Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli
09:16

Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli

Published on: April 5, 2019

11.4K

相关实验视频

Last Updated: Jan 13, 2026

Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders
05:49

Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders

Published on: November 1, 2024

1.2K
Monitoring Acupuncture Effects on Human Brain by fMRI
09:55

Monitoring Acupuncture Effects on Human Brain by fMRI

Published on: April 8, 2010

15.9K
Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli
09:16

Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli

Published on: April 5, 2019

11.4K

科学领域:

  • 神经科学是一个神经科学.
  • 人工智能的人工智能
  • 生物医学工程 生物医学工程

背景情况:

  • 准确的疼痛评估对于患者护理至关重要,特别是对于有沟通障碍的人来说.
  • 目前的疼痛监测方法通常依赖于主观报告,限制了它们在某些临床群体中的有效性.

研究的目的:

  • 通过使用可解释的深度学习模型,研究从脑电图 (EEG) 信号中解码疼痛水平的可行性.
  • 为客观疼痛评估开发一个独立于主体的方法.

主要方法:

  • 从50名经历低和高疼痛刺激的受试者收集了EEG数据.
  • 一个1D卷积神经网络 (CNN) 通过离开一个主体 (LOSO) 交叉验证进行了训练,以进行主体独立分类.
  • 使用DeepSHAP (夏普利添加式扩展) 来识别导致疼痛分类的频率特定EEG特征.

主要成果:

  • 该CNN模型实现了高分类准确率95.85%,超过了传统的机器学习分类器.
  • 可解释性分析显示,增高的β频段活动 (14-15 Hz) 与高疼痛相关.
  • 阿尔法 (11-12赫兹),和三角带活动与下部疼痛状态有关.

结论:

  • 可解释的深度学习提供了一个有希望的实时方法,从EEG解码主体独立的疼痛.
  • 这些发现支持将可解释的人工智能 (XAI) 技术集成到基于EEG的大脑计算机接口 (BCI) 系统中,以客观监测疼痛.