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

Autor: Zhang K; School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore. Electronic address: kzhang015@e.ntu.edu.sg., Robinson N; School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore. Electronic address: nrobinson@ntu.edu.sg., Lee SW; Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea. Electronic address: sw.lee@korea.ac.kr., Guan C; School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore. Electronic address: ctguan@ntu.edu.sg.
Jazyk: angličtina
Zdroj: Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2021 Apr; Vol. 136, pp. 1-10. Date of Electronic Publication: 2020 Dec 23.
DOI: 10.1016/j.neunet.2020.12.013
Abstrakt: 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.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2020 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
Externí odkaz: