Enhanced Motor Imagery Training Using a Hybrid BCI with Feedback

Tianyou Yu, Jun Xiao, Fangyi Wang, Rui Zhang, Zhenghui Gu, Andrzej Cichocki, Yuanqing Li

Research output: Contribution to journalArticlepeer-review

80 Citations (Scopus)


Goal: Motor imagery-related mu/beta rhythms, which can be voluntarily modulated by subjects, have been widely used in EEG-based brain computer interfaces (BCIs). Moreover, it has been suggested that motor imagery-specific EEG differences can be enhanced by feedback training. However, the differences observed in the EEGs of naive subjects are typically not sufficient to provide reliable EEG control and thus result in unintended feedback. Such feedback can frustrate subjects and impede training. In this study, a hybrid BCI paradigm combining motor imagery and steady-state visually evoked potentials (SSVEPs) has been proposed to provide effective continuous feedback for motor imagery training. Methods: During the initial training sessions, subjects must focus on flickering buttons to evoke SSVEPs as they perform motor imagery tasks. The output/feedback of the hybrid BCI is based on hybrid features consisting of motor imagery- and SSVEP-related brain signals. In this context, the SSVEP plays a more important role than motor imagery in generating feedback. As the training progresses, the subjects can gradually decrease their visual attention to the flickering buttons, provided that the feedback is still effective. In this case, the feedback is mainly based on motor imagery. Results: Our experimental results demonstrate that subjects generate distinguishable brain patterns of hand motor imagery after only five training sessions lasting approximately 1.5 h each. Conclusion: The proposed hybrid feedback paradigm can be used to enhance motor imagery training. Significance: This hybrid BCI system with feedback can effectively identify the intentions of the subjects.

Original languageEnglish
Article number7038194
Pages (from-to)1706-1717
Number of pages12
JournalIEEE Transactions on Biomedical Engineering
Issue number7
Publication statusPublished - 1 Jul 2015
Externally publishedYes


  • Brain-computer interface (BCI)
  • hybrid feature
  • motor imagery training
  • neuro-feedback
  • steady-state visually evoked potentials (SSVEPs)


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