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

Evaluating the sensitivity of dry and gel-based wearable EEG for cognitive load estimation.

Biomedical physics & engineering express·2026
Same author

Brainsourcing for temporal visual attention estimation.

Biomedical engineering letters·2025
Same author

Sneaky emotions: impact of data partitions in affective computing experiments with brain-computer interfacing.

Biomedical engineering letters·2024
Same author

No Interface, No Problem: Gesture Recognition on Physical Objects Using Radar Sensing.

Sensors (Basel, Switzerland)·2021
Same author

Glimpse: A Gaze-Based Measure of Temporal Salience.

Sensors (Basel, Switzerland)·2021
Same author

Cocaine-Induced Preference Conditioning: a Machine Vision Perspective.

Neuroinformatics·2018

相关实验视频

Updated: May 12, 2025

Combining Computer Game-Based Behavioural Experiments With High-Density EEG and Infrared Gaze Tracking
13:40

Combining Computer Game-Based Behavioural Experiments With High-Density EEG and Infrared Gaze Tracking

Published on: December 16, 2010

16.6K

从EEG预测固定和凝视位置.

Yoelvis Moreno-Alcayde1, V Javier Traver2, Luis A Leiva3

  • 1Institute of New Imaging Technologies, Universitat Jaume I, Av. Vicent Sos Baynat, s/n, 12071, Castellón, Spain.

Medical & biological engineering & computing
|May 8, 2025
PubMed
概括
此摘要是机器生成的。

研究人员使用脑电图 (EEG) 深度学习模型探索预测眼神凝视. 一种基于变压器的方法显示出有前途,性能优于LSTM,提供了无需额外设备的先进眼球追踪的洞察力.

关键词:
这是一个EEGEEGEEGEEGEEGEEGEEG.眼睛凝视着眼睛.固定方式 固定方式神经模型的神经模型

更多相关视频

Simultaneous Eye Tracking and Single-Neuron Recordings in Human Epilepsy Patients
07:43

Simultaneous Eye Tracking and Single-Neuron Recordings in Human Epilepsy Patients

Published on: June 17, 2019

7.7K
Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
09:27

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

9.8K

相关实验视频

Last Updated: May 12, 2025

Combining Computer Game-Based Behavioural Experiments With High-Density EEG and Infrared Gaze Tracking
13:40

Combining Computer Game-Based Behavioural Experiments With High-Density EEG and Infrared Gaze Tracking

Published on: December 16, 2010

16.6K
Simultaneous Eye Tracking and Single-Neuron Recordings in Human Epilepsy Patients
07:43

Simultaneous Eye Tracking and Single-Neuron Recordings in Human Epilepsy Patients

Published on: June 17, 2019

7.7K
Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
09:27

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

9.8K

科学领域:

  • 神经科学是一个神经科学.
  • 机器学习 机器学习
  • 生物医学工程 生物医学工程

背景情况:

  • 眼睛跟踪对于理解认知过程至关重要.
  • 传统的眼球追踪需要专门的设备.
  • 从脑电图 (EEG) 估计眼睛的凝视提供了一个非侵入性的替代方案.

研究的目的:

  • 通过使用EEG数据的深度学习模型来研究预测眼睛凝视和估计固定的可行性.
  • 探索空间和时间数据维度,局部与全球处理以及建筑设计对模型性能的影响.
  • 将拟议的基于变压器和LSTM的架构与在减少数据约束的情况下使用最先进的方法进行比较.

主要方法:

  • 开发和比较两个深度学习架构:基于变压器的和基于LSTM的.
  • 使用EEG信号对固定预测和目光估计任务的模型的评估.
  • 在缩短的EEG信号长度和频道数下测试模型的稳定性.

主要成果:

  • 基于变压器的模型表现优于只有LSTM的模型.
  • 这两种模型在从头开始训练时,都取得了与最先进的方法相当或比最先进的方法更好的结果.
  • 变压器模型对信号长度和通道缩短的灵敏度更高.

结论:

  • 深度学习模型,特别是变压器,可以有效地从EEG数据中预测眼睛的凝视.
  • 建筑设计的选择显著影响性能,特别是在数据限制下.
  • 这项研究提升了非侵入性,无设备眼睛跟踪解决方案的潜力.