Toward consistency between humans and classifiers: Improved performance of a real-time brain–computer interface using a mutual learning system

Chun Yi Lin, Chia Feng Lu, Chi Wen Jao, Po Shan Wang, Yu Te Wu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The performance of electroencephalography (EEG) classifiers in a brain–computer interface (BCI) depends heavily on the quality and consistency of training data. Therefore, facilitating collaboration between two independent systems, namely humans and machines, as well as increasing model generalizability and personalization are important tasks. In this study, we designed a mutual learning system to stabilize the EEG patterns of users who performed motor imagery (MI) and attention tasks, and we updated the parameters of a deep learning classifier in real time to improve consistency between the system and users. According to our results, the accuracy of the users on the MI task increased from 56% ± 13.9% to 81.5% ± 8.18%, and that on the attention task increased from 55% ± 7.07% to 82.5% ± 12.3% after application of the proposed mutual learning system. Thus, the mutual learning system facilitates the application of personalized BCIs and heralds a new era in which humans and machines can learn from each other.

Original languageEnglish
Article number120205
JournalExpert Systems with Applications
Volume226
DOIs
StatePublished - 15 Sep 2023

Keywords

  • Bio-feedback
  • Brain–computer interface
  • Convolutional neural network
  • End-to-end learning
  • Mutual learning system

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