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

Data Collection for Automatic Depression Identification in Spanish Speakers Using Deep Learning Algorithms: Protocol for a Case-Control Study.

JMIR research protocols·2025
Same author

Implementation of a Long Short-Term Memory Neural Network-Based Algorithm for Dynamic Obstacle Avoidance.

Sensors (Basel, Switzerland)·2024
Same author

Enhancing IoT Network Security: Unveiling the Power of Self-Supervised Learning against DDoS Attacks.

Sensors (Basel, Switzerland)·2023
Same author

Interpretable Classification of Tauopathies with a Convolutional Neural Network Pipeline Using Transfer Learning and Validation against Post-Mortem Clinical Cases of Alzheimer's Disease and Progressive Supranuclear Palsy.

Current issues in molecular biology·2022
Same author

Link Quality Estimation for Wireless ANDON Towers Based on Deep Learning Models.

Sensors (Basel, Switzerland)·2022
Same author

A Novel Automatic Quantification Protocol for Biomarkers of Tauopathies in the Hippocampus and Entorhinal Cortex of Post-Mortem Samples Using an Extended Semi-Siamese U-Net.

Biology·2022
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
Same journal

Three-Dimensional Modeling and Performance Analysis of Dynamic mmWave V2I Networks Based on Stochastic Geometry.

Sensors (Basel, Switzerland)·2026
查看所有相关文章

相关实验视频

Updated: Jun 3, 2025

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

43.2K

解锁基于脑电图的全面用户身份验证系统的安全性

Adnan Elahi Khan Khalil1, Jesus Arturo Perez-Diaz1, Jose Antonio Cantoral-Ceballos1

  • 1School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64700, Nuevo Leon, Mexico.

Sensors (Basel, Switzerland)
|January 8, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种基于神经网络的基于电脑电图 (EEG) 的新型身份验证系统. 该系统根据大脑信号准确识别和认证用户,达到97%的准确性,以提高安全性.

关键词:
许多LP神经网络的神经网络.P300 潜在的可能.电脑电图 (EEG) 是一个电脑电图.机器学习是机器学习.多因素身份验证是多因素身份验证.用户身份验证用户身份验证用户识别用户识别

更多相关视频

Simultaneous Video-EEG-ECG Monitoring to Identify Neurocardiac Dysfunction in Mouse Models of Epilepsy
11:54

Simultaneous Video-EEG-ECG Monitoring to Identify Neurocardiac Dysfunction in Mouse Models of Epilepsy

Published on: January 29, 2018

25.3K
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

2.2K

相关实验视频

Last Updated: Jun 3, 2025

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

43.2K
Simultaneous Video-EEG-ECG Monitoring to Identify Neurocardiac Dysfunction in Mouse Models of Epilepsy
11:54

Simultaneous Video-EEG-ECG Monitoring to Identify Neurocardiac Dysfunction in Mouse Models of Epilepsy

Published on: January 29, 2018

25.3K
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

2.2K

科学领域:

  • 神经科学是一个神经科学.
  • 计算机科学 计算机科学
  • 生物识别信息 生物识别信息

背景情况:

  • 由于人工智能的进步,对强大的安全系统的需求越来越大.
  • 对用于用户身份验证的脑信号 (EEG) 分析越来越感兴趣.
  • 以往基于EEG的方法在实现高精度方面的局限性.

研究的目的:

  • 开发和评估基于EEG的用户身份验证方案.
  • 利用P300潜力和一个多层感知器前神经网络 (MLP FFNN).
  • 在用户识别和身份验证方面实现高精度.

主要方法:

  • 使用电脑电图 (EEG) 信号,专注于P300电位.
  • 采用了多层感知前神经网络 (MLP FFNN).
  • 使用五个频段的功率光谱密度 (PSD) 相互信息 (MI) 的特征提取.
  • 两个阶段的过程:用户识别 (多类分类) 和用户身份验证 (概率评估).

主要成果:

  • 在基于EEG的用户识别中实现了97%的准确性.
  • 在基于EEG的用户身份验证中实现了97%的准确性.
  • 该计划可以容纳新的用户,而不需要再培训.

结论:

  • 拟议的基于EEG的身份验证方案提供了一种可靠和准确的方法来保护个人资产.
  • P300潜能,MLP FFNN和MI特征提取的组合提供了强大的身份验证.
  • 这种方法代表了使用脑-计算机接口的生物识别安全的重大进步.