TY - JOUR
T1 - Adaptive transfer learning for EEG motor imagery classification with deep Convolutional Neural Network
AU - Zhang, Kaishuo
AU - Robinson, Neethu
AU - Lee, Seong Whan
AU - Guan, Cuntai
N1 - Funding Information:
This work was partially supported by the RIE2020 Advanced Manufacturing and Engineering (AME) Programmatic Fund, Singapore (No. A20G8b0102). This work was partially supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (No. 2017-0-00451, and No. 2019-0-00079). The computational work for this project was partially performed on resources of the National Supercomputing Centre, Singapore (https://www.nscc.sg).
Funding Information:
This work was partially supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (No. 2017-0-00451 , and No. 2019-0-00079 ).
Funding Information:
This work was partially supported by the RIE2020 Advanced Manufacturing and Engineering (AME) Programmatic Fund , Singapore (No. A20G8b0102 ).
Publisher Copyright:
© 2020 Elsevier Ltd
PY - 2021/4
Y1 - 2021/4
N2 - 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.
AB - 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.
KW - Brain–computer interface (BCI)
KW - Convolutional Neural Network (CNN)
KW - Electroencephalography (EEG)
KW - Transfer learning
UR - http://www.scopus.com/inward/record.url?scp=85099248925&partnerID=8YFLogxK
U2 - 10.1016/j.neunet.2020.12.013
DO - 10.1016/j.neunet.2020.12.013
M3 - Article
C2 - 33401114
AN - SCOPUS:85099248925
SN - 0893-6080
VL - 136
SP - 1
EP - 10
JO - Neural Networks
JF - Neural Networks
ER -