脑-机接口
运动表象
计算机科学
管道(软件)
接口(物质)
人工智能
学习迁移
信号(编程语言)
人机交互
机器学习
模式识别(心理学)
脑电图
心理学
气泡
最大气泡压力法
精神科
并行计算
程序设计语言
作者
Dongrui Wu,Xue Jiang,Ruimin Peng
标识
DOI:10.1016/j.neunet.2022.06.008
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI