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

Introduction to Cognitive Psychology01:20

Introduction to Cognitive Psychology

474
Cognitive psychology is the field of psychology dedicated to examining how people think. It attempts to explain how and why we think the way we do by studying the interactions among human thinking, emotion, creativity, language, and problem-solving, as well as other cognitive processes. Cognitive psychology studies how information is processed and manipulated in remembering, thinking, and knowing.
This field emerged in the mid-20th century, following a period dominated by behaviorism, which...
474

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

A Multi-Head Attention Transformer Model for Wearable in Situ Fall Detection.

IEEE access : practical innovations, open solutions·2026
Same author

EMG-projected MEG high-resolution source imaging of human motor execution: Brain-muscle coupling above movement frequencies.

Imaging neuroscience (Cambridge, Mass.)·2024
Same author

Non-invasive ventral cervical magnetoneurography as a proxy of in vivo lipopolysaccharide-induced inflammation.

Communications biology·2024
Same author

EMG-projected MEG High-Resolution Source Imaging of Human Motor Execution: Brain-Muscle Coupling above Movement Frequencies.

medRxiv : the preprint server for health sciences·2023
Same author

TOWARDS MUSCULOSKELETAL SIMULATION-AWARE FALL INJURY MITIGATION: TRANSFER LEARNING WITH DEEP CNN FOR FALL DETECTION.

Spring simulation conference (SpringSim)·2023
Same author

Real-Time Indoor Geolocation Tracking for Assisted Healthcare Facilities.

International journal of interdisciplinary telecommunications and networking·2023

相关实验视频

Updated: Jun 26, 2025

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots
11:01

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots

Published on: November 24, 2015

13.2K

一个层次性的递归特征消除算法,用于开发脑计算机界面应用,用于统计推理和决策的用户行为.

Shams Al Ajrawi1, Ramesh Rao2, Mahasweta Sarkar3

  • 1Department of Electrical and Computer Engineering, University of California, San Diego, Alliant International univercity, San Diego, CA, USA; CSML, Alliant International University, San Diego, San Diego, CA, USA; Department of Electrical and Computer Engineering, San Diego State University, San Diego, CA, USA.

Journal of neuroscience methods
|May 8, 2024
PubMed
概括

一种新的等级递归特征消除 (HRFE) 方法通过有效处理杂的大脑信号来增强脑计算机接口 (BCI) 的分类. 这种计算机视觉方法在电皮质谱 (ECoG) 信号中实现了93%的可靠性.

关键词:
大脑计算机接口 (BCI)电皮质谱学 电皮质谱学 电皮质谱学特性,提取方式机器学习 (ML) 是指机器学习.多个分类器可以分类.

更多相关视频

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

562
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

2.3K

相关实验视频

Last Updated: Jun 26, 2025

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots
11:01

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots

Published on: November 24, 2015

13.2K
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

562
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

2.3K

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
  • 生物医学工程 生物医学工程

背景情况:

  • 大脑-计算机接口 (BCI) 允许用户通过大脑信号来控制智能设备.
  • 由于高维度,噪音大脑信号数据,BCI分类面临挑战.
  • 计算机视觉技术对于处理和分类这些复杂的信号至关重要.

研究的目的:

  • 开发和评估一个新的特征选择方法,用于BCI分类.
  • 解决大脑信号处理中噪音和高维度的挑战.
  • 提高BCI系统的可靠性和准确性.

主要方法:

  • 引入了一种层次递归特征消除 (HRFE) 方法来处理杂的大脑信号.
  • 应用HRFE来分类来自两个BCI数据集 (数据集I和BCI比赛III) 的电皮质谱 (ECoG) 信号.
  • 使用HRFE的浅和深卷积神经网络分类技术.

主要成果:

  • 从ECoG信号中选择了影响分类的前20个特征.
  • 与现有方法相比,HRFE显示了显著的计算机视觉增强.
  • 在ECoG信号分类中实现了大约93%的可靠性.

结论:

  • HRFE是BCI分类中特征选择的有效方法,特别是对于噪音高的ECoG数据.
  • 拟议的方法在分类可靠性方面提供了显著的改善.
  • 这项工作有助于提高BCI系统的性能和适用性.