Online Adaptive CNN: a Session-to-session Transfer Learning Approach for Non-stationary EEG

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Online Adaptive CNN: a Session-to-session Transfer Learning Approach for Non-stationary EEG
Title:
Online Adaptive CNN: a Session-to-session Transfer Learning Approach for Non-stationary EEG
Journal Title:
IEEE Symposium Series on Computational Intelligence
DOI:
Publication URL:
Keywords:
Publication Date:
05 December 2022
Citation:
Zhang, S., Zheng, D., Tang, N., Chew, E., Lim, R. Y., Ang, K. K., & Guan, C. (2022). IEEE Symposium Series on Computational Intelligence. In Online Adaptive CNN: a Session-to-session Transfer Learning Approach for Non-stationary EEG (pp. 164–170). Singapore; IEEE.
Abstract:
The convolutional neural network (CNN) automatically learns EEG representations in higher and nonlinear space via backpropagation and outputs the predictions in an end-to-end manner. Owing to these advantages, CNN has been used to decode electroencephalogram (EEG) and drive brain computer interface (BCI). However, its applications in BCI-assisted poststroke neurorehabilitation remain limited for it is unable to address the inherent session-to-session non-stationarity in the EEG between the initial calibration session and subsequent online sessions. In this paper, we present a simple but effective online adaptive CNN (aCNN) to address the non-stationarity in multisession EEG by progressively updating the subject-specific model. The performance of the proposed aCNN is evaluated on two neurorehabilitation datasets with a large population of poststroke patients (33 patients with a total of 358 EEG sessions). Results indicate that, our proposed aCNN reaches at least as good a performance as the widely used online adaptive Filter Bank Common Spatial Patterns (aFBCSP) and with significantly higher accuracies than that for DeepConv and offline FBCSP algorithms. Our results support, for the first time, the use of a CNN-based adaptive learning method to decode non-stationary EEG signals for BCI-assisted post-stroke rehabilitation.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - RIE2020 AME Programmatic Fund
Grant Reference no. : A20G8b0102

This research was funded by National Natural Science Foundation of China (Grant No. 61873021; 62088101), the China Scholarship Council (Grant No. 202006020219), the special fund for basic scientific research in central colleges and universities-Youth talent support program of Beihang University and the Key Laboratory of Precision Opto-mechatronics Technology (Beihang University), Ministry of Education, China.
Description:
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
978-1-6654-8768-9/22
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