**5. Our proposed model**

Based on our review in this chapter, we summarize three elements that should be considered in the design of a BCI training protocol:

• Feedback should be realistic and compatible with the task content. Particularly, in a motor imagery-based BCI, users would benefit from observation of movements that are consistent with their mental images.

training protocol is highly influenced by the sense of embodiment that participants per-

**Figure 3.** Subjects who were trained with a humanlike android robot and experienced a high sense of embodiment revealed a stronger mu suppression in the sensorimotor areas and showed better BCI performance compared to subjects

Brain-Computer Interface and Motor Imagery Training: The Role of Visual Feedback and Embodiment

http://dx.doi.org/10.5772/intechopen.78695

83

In this chapter, we highlighted the importance of a human user in the BCI loop and addressed some of the deficiencies in the training and feedback design of the classical motor imagery-based BCI systems. We provided empirical evidence that a careful training design that views BCI experience from the user's perspective and considers such factors as task-feedback compatibility, motivation, and embodiment could reinforce users' learning of the motor imagery task and consequently improve their BCI performance in a very short amount of time. We believe that our results are of importance to the BCI community and should be taken into account for future design of BCI systems that are employed in real-world applications outside of laboratories.

This research was supported by Grants-in-Aid for Scientific Research 25220004, 26540109, and

ceived during BCI control of the robot's hands.

who were trained with a classical feedback bar.

**6. Conclusion**

**Acknowledgements**

**Conflict of interest**

The authors declare no conflicts of interest.

15F15046.


By integrating the knowledge we obtained in our previous experiments [8, 22, 62] and the abovementioned points, we proposed an android-based training protocol in [65]. In this study, two groups of novice participants practiced hand grasp imagery either by a classical cue-based feedback (arrow and feedback bar) or by watching first-person perspective images of a humanlike android robot that made hand grasps based on the subject's EEG patterns (**Figure 3**). In addition, subjects' performance was positively biased during the training phase in order to boost their confidence and motivation for the motor imagery task. More importantly, we added a pre-training phase for the android group, where subjects could practice motor imagery, followed by kinesthetic motor actions. Results from this study revealed that participants who were trained with an android-based BCI achieved a significantly higher mu suppression in the sensorimotor areas (C3/C4 scalp positions) as well as a significantly better online BCI performance in the final evaluation phase compared to the participants who were trained with a classical training paradigm. We believe that the improved modulation of the sensorimotor rhythms in the proposed Brain-Computer Interface and Motor Imagery Training: The Role of Visual Feedback and Embodiment http://dx.doi.org/10.5772/intechopen.78695 83

**Figure 3.** Subjects who were trained with a humanlike android robot and experienced a high sense of embodiment revealed a stronger mu suppression in the sensorimotor areas and showed better BCI performance compared to subjects who were trained with a classical feedback bar.

training protocol is highly influenced by the sense of embodiment that participants perceived during BCI control of the robot's hands.
