A Deep Transfer Learning Training Strategy For Inter-Subject Classification of EEG Signal
2021 28th National and 6th International Iranian Conference on Biomedical Engineering (ICBME)(2021)
摘要
Electroencephalogram(EEG) signals are generally available in small limited quantities, and there are considerable variabilities between individual and recording sessions. Thus it is crucial to obtain a model capable of finding discriminative features invariant to these differences. For this purpose, Convolutional Neural Networks (CNN) proved to be suited for detecting Spatio-temporal features that are discriminative for these signals. This paper introduces Stage-training as a variation of fine-tuning to train end-to-end CNN models explicitly designed for learning discriminative features from EEG recordings. The Stage-training approach specifically addresses the challenges of finding a model invariant to inter-subject differences when fine-tuning fails due to the particular structure of these models. Using this approach, which conducts multiple stages of fine-tuning in transfer learning, we significantly increased the overall accuracy of the EEGNet by 9%. Our strategy's implementation can be found at: https://github.com/mj-sam/stage-trans.
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关键词
Brain-computer interface,Transfer Learning,EEG,Convolution neural network,Deep learning
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