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Title: Adaptive transfer learning for EEG motor imagery classification with deep convolutional neural network
Authors: Zhang, Kaishuo
Robinson, Neethu
Lee, Seong-Whan
Guan, Cuntai
Keywords: Engineering::Computer science and engineering
Issue Date: 2021
Source: Zhang, K., Robinson, N., Lee, S. & Guan, C. (2021). Adaptive transfer learning for EEG motor imagery classification with deep convolutional neural network. Neural Networks, 136, 1-10.
Project: No. A20G8b0102
Journal: Neural Networks 
Abstract: 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.
ISSN: 0893-6080
DOI: 10.1016/j.neunet.2020.12.013
Schools: School of Computer Science and Engineering 
Rights: © 2020 Elsevier Ltd. All rights reserved
Fulltext Permission: none
Fulltext Availability: No Fulltext
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