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A transfer learning-based feedback training motivates the performance of SMR-BCI
Xu Duan1,2, Songyun Xie1, Yanxia Lv1
1School of Electronics and Information, Northwestern Polytechnical University, Dongxiang Road 1, Xi'an 710129, People's Republic of China.
Journal of Neural Engineering
|December 28, 2022
Summary
Transfer learning (TL) feedback enhances brain-computer interface (BCI) learning by boosting user motivation through past performance context. This method improved sensorimotor rhythm (SMR) modulation and BCI control performance compared to standard feedback.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Human-Computer Interaction
Background:
- Brain-computer interface (BCI) systems rely on users modulating sensorimotor rhythms (SMRs) for control.
- User motivation significantly impacts BCI learning, yet is often underutilized in feedback strategies.
- Existing feedback methods may not optimally engage users psychologically during BCI training.
Purpose of the Study:
- To investigate a novel transfer learning (TL) feedback method designed to enhance user self-motivation in SMR-BCI.
- To compare the efficacy of TL feedback against traditional cursor-bar (CB) feedback for BCI training.
Main Methods:
- Developed a TL feedback system displaying past EEG performance alongside current signals to provide motivational context.
- Conducted a between-subject experiment with 24 healthy, BCI-naive participants using either TL or CB feedback for SMR-BCI training over three sessions.
- Participants imagined left- and right-hand movements to control the BCI.
Main Results:
- TL feedback demonstrated improved class distinctiveness and EEG discriminancy.
- User motivation, indicated by challenge and mastery confidence, increased throughout TL feedback training.
- BCI performance with TL feedback was 60.5% higher in the final session compared to CB feedback.
Conclusions:
- The proposed TL feedback method effectively boosts psychological engagement and self-motivation in BCI users.
- TL feedback facilitates more effective SMR modulation and superior BCI control performance.
- TL feedback presents a promising alternative to conventional BCI feedback paradigms.
