Data Augmentation: Using Channel-Level Recombination to Improve Classification Performance for Motor Imagery EEG

Data Augmentation: Using Channel-Level Recombination to Improve Classification Performance for Motor Imagery EEG

The quality and quantity of training data are crucial to the performance of a deep-learning-based brain-computer interface (BCI) system. However, it is not practical to record EEG data over several long calibration sessions. A promising time- and cost-efficient solution is artificial data generation...

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Bibliographic Details
Main Authors: Yu Pei, Zhiguo Luo, Ye Yan, Huijiong Yan, Jing Jiang, Weiguo Li, Liang Xie, Erwei Yin
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-03-01
Series:Frontiers in Human Neuroscience
Subjects:
brain-computer interface
electroencephalogram
motor imagery
deep learning
inter-subject transfer learning
pre-training
Online Access:https://www.frontiersin.org/articles/10.3389/fnhum.2021.645952/full

Internet

https://www.frontiersin.org/articles/10.3389/fnhum.2021.645952/full

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