**3. Human factors and BCI learning**

report that the observation of a left- or a right-hand movement could also lead to high classification accuracies at parieto-occipital regions [27]. Many neuroimaging studies have found empirical evidences that combining motor imagery with action observation could induce a stronger cortical activation compared to either condition alone [28]. This has been associated with the firing of mirror neurons [29] that become active during the observation of a motion and represent high-level information about goals and intentions [28]. It also indicates a shared neurocognitive process between motor imagery and action observation that could be utilized in BCI training and control. Particularly, if the action is congruent with the motor imagery, the observed image is a simulation of one's own action, the combination of the two conditions can lead to a "sense of effort," a sense of agency, and imagined kinesthetic sensations that would

Ono et al. examined the effect of visual feedback on ERD during a motor imagery task [31]. In a series of training sessions, they hired different groups of subjects and trained them on different types of visual feedback, including a conventional feedback bar, a human hand that was shown on a screen in front of the subject and a human hand that was shown on a screen as the extension of one's own arm. They found that by the end of the training, the group that was presented anatomically congruent feedback produced the highest ERD value and classification accuracy. Neuper et al. have also investigated the impact of a visual feedback presentation on sensorimotor EEG rhythms and BCI performance [32]. They trained two groups of subjects on a motor imagery-based BCI using two feedback designs: a realistic feedback (a video of a moving hand that grasped a glass) and an abstract feedback (a moving bar that extended horizontally). Their results, however, showed no difference between the two feedback groups in terms of motor imagery learning and ERD changes. An explanation for this, as authors have indicated in their

discussion, could be the short training period and few number of feedback sessions.

With recent advancement in videogames and VR technology, a more rich, realistic, and engaging visual presentation of the BCI output has become possible. Pineda et al. designed a threedimensional first-person shooter game that enabled BCI users to make navigational movements by left and right motor imageries [33]. Their results indicated that subjects could learn to control levels of mu rhythm very quickly, within approximately 3–10 hours of training that was scheduled over a course of five weeks. Leeb et al. also reported a case study with a tetraplegic patient who was able to navigate through VR by imagination of his feet movements that was translated into movements of an avatar [34]. The most obvious benefits of VR in the construction of visual feedback are the richness of details that could be incorporated, the sense of embodiment it induces (see Section 4), and a relatively low cost. Particularly, in terms of detailed feedback, it can involve different types of movement and inclusion of goal-oriented tasks. Past studies have shown that motor cortex is sensitive to different forms of observed motor behavior [35] and subjective perspective [30, 36]. Muthukumaraswamy et al. have shown that the observation of an object-directed precision grip produces more mu suppression than the observation of a nonobject-directed grip [35]. In our previous study, we compared motor imagery learning between two groups of BCI users who operated either a pair of robotic gripper or a pair of humanlike robotic hands [8]. We found a more robust learning of the BCI task for the second group who were trained with a pair of humanlike robotic hands. This result provides evidence that visual

arise during one's own motor execution [30].

76 Evolving BCI Therapy - Engaging Brain State Dynamics

To control a BCI, the user has to perform a mental imagery task and generate distinguishable brain activity for signal-processing algorithms. Modulation of one's own brain signals is not an intuitive task, and therefore the user needs to practice and learn the BCI "skill." However, an efficient learning of a skill requires optimized training protocols that consider the user's psychological states (such as motivation, attention, confidence, and satisfaction) in order to ensure more effort and better performance from the user's side [44]. Kleih et al. have shown that in the control of a P300 BCI, the level of P300 amplitude was significantly correlated with the level of self-rated motivation, that is, highly motivated subjects were able to communicate through BCI faster than less motivated subjects [45]. In another BCI study with ALS patients, Nijboer et al. reported that motivational factors, specifically challenge and confidence, were positively correlated with BCI performance, whereas fear had a negative influence [46]. It is suggested that even with highly motivated subjects, users can experience a low level of satisfaction if they do not succeed in accomplishing the BCI-control task [47].

In order to overcome such issues, many researchers have explored alternative BCI training protocols. Leeb et al. suggested employment of VR environments in designing attractive BCI training paradigms that increase user's engagement [18]. Their results show that users are likely to perform better in a VR navigation task compared to the conventional training with cue-based feedback. Lotte et al. proposed improvement of engagement and motivation in a social context by the application of a BCI game between two users [44]. Users could either participate in a collaborative game, where the sum of the BCI outputs from both users was used to direct a ball on a screen, or in a competitive version, where the users had to push the ball toward the opposite direction. They observed that multiplayer version of the games could effectively improve BCI performance compared to its single player version.

sensory and motor experiences [52, 53]. Under this view, the mind (mental images, thoughts, representation) is created from processes that are closely related to brain representations of the body and the way it interacts with the real world [54]. This fosters the notion of neural plasticity during the learning of new motor skills and tool use that might lead to temporary or long-term incorporation of new objects and augmented cognition [55]. When extended to external body parts (dummy limbs), the experience of embodiment is often described by the two senses of body ownership (to what extent the seen body part was perceived as one's own body) and agency (to what extent the motions of the seen body were attributed to one's own movements) [56]. Although there are some counter arguments [57], embodiment is generally conceived as an important component in establishing interaction between a patient and medical BCIs (such as neural prostheses) for better incorporation of the artificial limb [58]. However, with the recent advancements in VR and robotic technology, the concept of embodiment has also been proposed as a reinforcing factor for immersive experience

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

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

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The first question, however, is whether BCI control of a non-body object would evoke a sense of embodiment for the operator. Here, we mainly focus on the sense of embodiment that is induced over a humanlike body shape rather than embodiment in physical space and for general objects as it is reported in [59]. Perez-Marcos et al. combined virtual reality and a motor imagery-based BCI in order to induce a sense of ownership for a virtual hand [60]. Although they did not assess motor-related features of the collected EEG signals in this study, they showed that BCI control of a virtual hand could induce an illusion of body ownership and trigger an electromyogram (EMG) response when the virtual hand suddenly fell down. Using a real-time fMRI setup, Cohen et al. also proposed a robotic embodiment for a humanoid robot in France that was remotely controlled by subjects performing motor imagery in Israel [61]. While they did not perform a systematic evaluation of the sense of embodiment and the number of subjects was limited, post-experiment interviews indicated a high level of tele-presence and embodiment for at least two of the four subjects who par-

In a similar direction, the authors of this chapter have reported an illusion of body ownership for a pair of humanlike robotic hands that were controlled by a BCI system [62]. In this experiment, subjects watched robot's hands from a first-person perspective in a headmounted display and performed a right or a left motor imagery in order to grasp a lighted ball inside the robot's hands (**Figure 1**). Our subjective (questionnaire) and physiological measurements (skin conductance response) revealed that the subjects experienced a feeling of owning the robot's hands, and this feeling had a significant correlation with their BCI

In addition to the enhancement of the immersive experience, the feeling of embodiment has been shown to have a positive impact on neurofeedback training and motor imagery learning at the neural level. Braun et al. reported a sense of ownership for an anthropomorphic robotic hand that was placed in front of the subjects and was controlled by a right motor imagery task [63]. Interestingly, their results indicated a stronger ERD in alpha and beta frequency bands when the robotic hand was in a congruent position (higher embodiment) compared to

of healthy users.

ticipated in this study.

performance [22].

Multimodality and closing the sensorimotor loop has also been suggested as another method to increase user's engagement and performance. Jeunet et al. compared users' performances in a motor imagery-based multi-task BCI with different feedback modalities (visual vs. tactile) and found a significant improvement when subjects received continuous tactile feedback compared to an equivalent visual feedback [48]. This is consistent with the study in [16] where haptic feedback, provided in a synchronized manner with the subject's execution of a motor imagery task, could facilitate decoding of movement intentions and increase classification accuracy for both healthy and stroke patients.

In addition to the above strategies, some studies have proposed manipulation of the feedback either by biasing the feedback accuracy (i.e., giving the user a perception that he/she did better/worse than what he/she actually did) or by error-ignoring (i.e., presenting feedback only when the user performed the task correctly) [21, 22, 49, 50]. Barbero et al. investigated the influence of a biased feedback on BCI performance when subjects navigated a falling ball on a screen by right- and left-hand imageries. They found that subjects with a poor performance benefitted from positive biasing of their performance level, whereas for those already capable of the BCI task, the bias of feedback impeded the results [21]. This is while Gonzalez-Franco et al. found larger learning effects for negative feedback than for positive feedback [49]. In our previous studies with BCI operation of a pair of humanlike robotic hands, we found a general improving effect, both when subjects received a positively biased feedback of their BCI performance and when their mistakes were not presented to them, that is, error ignoring [22]. This improvement could have been associated with the higher sense of embodiment that users experienced during operation of the hands (see Section 4).

Overall, previous research demonstrates that human psychological factors play a significant role in the process of BCI training. It is even suggested that parameters such as personality, motivation, and attention span could predict performance in a single session of motor imagery-based BCI control [51]. Future training environments should take these parameters into account in order to enhance learning of the BCI task as well as to address the problem of "BCI inefficiency" that concerns users who are unable to learn BCI control.
