Adaptive transfer learning for EEG motor imagery classification with deep Convolutional Neural Network

In recent years, deep learning has emerged as a powerful tool for developing Brain–Computer Interface (BCI) systems. However, for deep learning models trained entirely on the data from a specific individual, the performance increase has only been marginal owing to the limited availability of subject...

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Vydané v:Neural networks Ročník 136; s. 1 - 10
Hlavní autori: Zhang, Kaishuo, Robinson, Neethu, Lee, Seong-Whan, Guan, Cuntai
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Elsevier Ltd 01.04.2021
Predmet:
Adult
Algorithms
Brain - physiology
Brain-Computer Interfaces - classification
Brain–computer interface (BCI)
Convolutional Neural Network (CNN)
Electroencephalography (EEG)
Electroencephalography - classification
Electroencephalography - methods
Female
Hand - physiology
Humans
Imagination - physiology
Machine Learning - classification
Male
Neural Networks, Computer
Psychomotor Performance - physiology
Transfer learning
Transfer, Psychology - physiology
Young Adult
Electroencephalography (EEG)
Transfer learning
Brain–computer interface (BCI)
Convolutional Neural Network (CNN)
ISSN:0893-6080, 1879-2782, 1879-2782
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Shrnutí:In recent years, deep learning has emerged as a powerful tool for developing Brain–Computer Interface (BCI) systems. However, for deep learning models trained entirely on the data from a specific individual, the performance increase has only been marginal owing to the limited availability of subject-specific data. To overcome this, many transfer-based approaches have been proposed, in which deep networks are trained using pre-existing data from other subjects and evaluated on new target subjects. This mode of transfer learning however faces the challenge of substantial inter-subject variability in brain data. Addressing this, in this paper, we propose 5 schemes for adaptation of a deep convolutional neural network (CNN) based electroencephalography (EEG)-BCI system for decoding hand motor imagery (MI). Each scheme fine-tunes an extensively trained, pre-trained model and adapt it to enhance the evaluation performance on a target subject. We report the highest subject-independent performance with an average (N=54) accuracy of 84.19% (±9.98%) for two-class motor imagery, while the best accuracy on this dataset is 74.15% (±15.83%) in the literature. Further, we obtain a statistically significant improvement (p=0.005) in classification using the proposed adaptation schemes compared to the baseline subject-independent model.
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content type line 23
ISSN:0893-6080
1879-2782
1879-2782
DOI:10.1016/j.neunet.2020.12.013