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

• Published in: Neural networks Vol. 136; pp. 1 - 10
• Main Authors: Zhang, Kaishuo, Robinson, Neethu, Lee, Seong-Whan, Guan, Cuntai
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.04.2021
• Subjects: 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
• DOI: 10.1016/j.neunet.2020.12.013

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.