.png){kind=icon}
返回
Transfer learning for motor imagery based brain-computer interfaces: A tutorial
.png){kind=icon}
OA
AI
DOI：10.1016/j.neunet.2022.06.008.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
A brain-computer interface (BCI) enables a user to communicate directly with an external device, e.g., a computer, using brain signals. It can be used to research, map, assist, augment, or repair human cognitive or sensory-motor functions. A closed-loop BCI system performs signal acquisition, temporal filtering, spatial filtering, feature engineering and classification, before sending out the control signal to an external device. Transfer learning (TL) has been widely used in motor imagery (MI) based BCIs to reduce the calibration effort for a new subject, greatly increasing their utility. This tutorial describes how TL can be considered in as many components of a BCI system as possible, and introduces a complete TL pipeline for MI-based BCIs. Examples on two MI datasets demonstrated the advantages of considering TL in multiple components of MI-based BCIs. Especially, integrating data alignment and sophisticated TL approaches can significantly improve the classification performance, and hence greatly reduces the calibration effort.
Keyword:
Brain-computer interface
Electroencephalogram
Transfer learning
Euclidean alignment
Motor imagery
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.3
论文数:
8.2K
被引数:
3.0W
机构
暂无机构信息
引用论文
.png){kind=icon}
分享收藏Managing Complex Mixtures of Chemicals – A Forward Look From The Regulators’ Perspective管理复杂的化学品混合物 -- 从监管机构的角度展望
PREAI
分享收藏A P300-based brain-computer interface: Initial tests by ALS patientsP300-based的脑机接口: ALS患者的初步测试
PREAI
分享收藏
查看更多内容