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

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

IEEE Symposium Series on Computational Intelligence

Shuailei Zhang, Dezhi Zheng, Ning Tang, Effie Chew, Rosary Lim, Kai Keng Ang, Cuntai Guan

05 December 2022

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.

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.

Publisher Copyright

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.

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https://oar.a-star.edu.sg/communities-collections/articles/18882

978-1-6654-8768-9/22

Institute for Infocomm Research