Classification of motor imagery electroencephalogram signals by using adaptive cross-subject transfer learning

Classification of motor imagery electroencephalogram signals by using adaptive cross-subject transfer learning

IntroductionElectroencephalogram (EEG)-based motor imagery (MI) classification is an important aspect in brain-computer interfaces (BCIs), which bridges between neural system and computer devices decoding brain signals into recognizable machine commands. However, due to the small number of training...

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Bibliographic Details
Main Authors: Jin Feng, Yunde Li, Chengliang Jiang, Yu Liu, Mingxin Li, Qinghui Hu
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-12-01
Series:Frontiers in Human Neuroscience
Subjects:
brain-computer interface
motor imagery
cross-subject transfer learning
kernel mean matching
TrAdaBoost
Online Access:https://www.frontiersin.org/articles/10.3389/fnhum.2022.1068165/full

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https://www.frontiersin.org/articles/10.3389/fnhum.2022.1068165/full

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