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

Kaishuo Zhang, Neethu Robinson, Seong Whan Lee, Cuntai Guan

Research output: Contribution to journalArticlepeer-review

157 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1-10
Number of pages10
JournalNeural Networks
Volume136
DOIs
Publication statusPublished - 2021 Apr

Bibliographical note

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

Keywords

  • Brain–computer interface (BCI)
  • Convolutional Neural Network (CNN)
  • Electroencephalography (EEG)
  • Transfer learning

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence

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