**6. Conclusion**

imagery conditions. This finding suggests that multisensory mechanisms related to the sense of body ownership and embodiment share neural processes with motor imagery and thus could be used in the activation and classification of EEG patterns in BCIs. Indeed, the two processes have been shown to go hand in hand as in [70], we demonstrated that the BCI control of a pair of humanlike robotic hands by means of motor imagery induces a higher sense of body ownership and agency compared to a direct control by means of motor execution. It could be speculated that because of the shared mechanism between embodiment and motor imagery, there is a positive loop effect: motor imagery of the hands induces a strong sense of embodiment and embodiment activates more motor-related neurons detectable by the BCI classifier.

Based on our review in this chapter, we summarize three elements that should be considered

• 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

• Human factors such as motivation, confidence, and fatigue can significantly affect user's interaction with the BCI system and subsequently influence their performance in the BCI task. Employment of interactive environments such as VR and providing positively biased feedback are two techniques that can enhance motor imagery learning particularly for nov-

• The sense of embodiment and body ownership establishes a positive interaction with subjects' motor imagery performance, and therefore, it is important to provide a realistic visual feedback that resembles a human body in terms of appearance, movement, and perspective.

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

**5. Our proposed model**

with their mental images.

ice BCI users.

in the design of a BCI training protocol:

82 Evolving BCI Therapy - Engaging Brain State Dynamics

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.
