首页|Transfer Learning in Motor Imagery Brain Computer Interface:A Review
Transfer Learning in Motor Imagery Brain Computer Interface:A Review
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
万方数据
Transfer learning,as a new machine learning methodology,may solve problems in related but different domains by using existing knowledge,and it is often applied to transfer training data from another domain for model training in the case of insufficient training data.In recent years,an increasing number of researchers who engage in brain-computer interface(BCI),have focused on using transfer learning to make most of the available electroencephalogram data from different subjects,effectively reducing the cost of expensive data acquisition and labeling as well as greatly improving the learning performance of the model.This paper surveys the development of transfer learning and reviews the transfer learning approaches in BCI.In addition,according to the"what to transfer"question in transfer learning,this review is organized into three contexts:instance-based transfer learning,parameter-based transfer learning,and feature-based transfer learning.Furthermore,the current transfer learning applications in BCI research are summarized in terms of the transfer learning methods,datasets,evaluation performance,etc.At the end of the paper,the questions to be solved in future research are put forward,laying the foundation for the popularization and in-depth research of transfer learning in BCI.
transfer learningbrain-computer interface(BCI)electroencephalogrammachine learning
李明爱、许冬芹
展开 >
Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China
Beijing Key Laboratory of Computational Intelligence and Intelligent System,Beijing 100124,China
Engineering Research Center of Digital Community,Ministry of Education,Beijing 100124,China
NETL
NSTL
万方数据
