Adaptive transfer learning for EEG motor imagery classification with deep Convolutional Neural Network. (April 2021)

• Record Type: Journal Article
• Authors: Zhang, Kaishuo; Robinson, Neethu; Lee, Seong-Whan; Guan, Cuntai
• Is Part Of: Neural networks. Volume 136(2021)
• Page Start: 1
• Page End: 10
• Publication Date: 2021-04
• Subjects: Transfer learning -- Brain–computer interface (BCI) -- Electroencephalography (EEG) -- Convolutional Neural Network (CNN); Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2020.12.013
• Languages: English
• ISSNs: 0893-6080

Abstract

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.