**4. The role of embodiment**

Recent view of cognitive development suggests that our cognitive skills are dynamically shaped through our bodily interaction with the environment and thus are grounded in 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 of healthy users.

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

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

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

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

Recent view of cognitive development suggests that our cognitive skills are dynamically shaped through our bodily interaction with the environment and thus are grounded in

"BCI inefficiency" that concerns users who are unable to learn BCI control.

effectively improve BCI performance compared to its single player version.

accuracy for both healthy and stroke patients.

78 Evolving BCI Therapy - Engaging Brain State Dynamics

experienced during operation of the hands (see Section 4).

**4. The role of embodiment**

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 participated in this study.

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 performance [22].

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

**Figure 1.** Users controlled a pair of humanlike robotic hands by performing right- and left-hand imageries while watching first-person perspective images of the robot's body.

an incongruent condition. Leeb et al. also compared the influence of feedback types on the motor imagery performance and BCI classification accuracy. They found that an immersive feedback (walking inside a VR environment) resulted in a better task performance by the subjects than a simple BCI feedback (bar presented on a computer screen), although this did not seem to affect the BCI classification accuracy [64].

The results obtained from the above studies are all consistent with our previously reported findings in [8] where subjects practiced motor imagery task in a BCI-control session with two types of feedback (**Figure 2A**). As mentioned earlier in this chapter, subjects who were trained with a more humanlike android robot could perform better on the motor imagery task in the final BCI-control session than those who were trained with a pair of metallic gripper (**Figure 2B**). In this study, "motor imagery performance" was defined as how well subjects could generate discriminant brain patterns for the two classes of right and left motor imageries and it was obtained by the Fisher's discriminant criterion in a linear discriminant analysis that observed the distribution of EEG features [8]. The ΔMotor imagery performance in **Figure 2B** represents the ratio of this criterion between the two evaluations and training sessions (for more details, refer to [8]). In another study, we also reported that in comparison with a classical feedback bar, motor imagery training with a humanlike android feedback that induces a sense of embodiment could lead to a stronger mu suppression in the sensorimotor areas and eventually improved subjects' online BCI performance [65].

[11]. The usage of a humanlike android in our studies could have influenced motor imagery learning twofold. First, it is speculated that the visual feedback provided from the android's body resembled a self-body action—as we experience it in our daily activities—and therefore matched with the visual anticipations of the motor intentions. Second, a more detailed and compatible visual feedback from the android's body (in terms of appearance and motion) could have excited motor memories more intensely, and therefore subjects trained with a humanlike android recalled more vivid and explicit images of the movement during the

**Figure 2.** Effect of embodiment on motor imagery learning. (A) Two groups of subjects practiced motor imagery task while receiving visual feedback from a humanlike android robot and a pair of metallic gripper. (B) Subjects who were trained with the android robot demonstrated a significantly more robust learning of the motor imagery task compared

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

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

81

Not only that embodiment can reinforce learning of the motor imagery and BCI task, it has also been shown that the two share spectral and anatomical mechanisms [68]. In the study of [68], subjects watched either a pair of virtual arms or a pair of non-body objects projecting out from their body inside a head-mounted display. For both visual feedbacks, they first received a visuotactile stimulation to experience a body ownership illusion similar to rubber hand illusion (RHI) [69], and then they were instructed to perform a motor imagery for either their right or left hand. Their overall results demonstrated that both illusory hand ownership and motor imagery were associated with a mu-band modulation, and more importantly, there was an overlap between the areas that were activated during illusory hand ownership and motor

imagery task [8].

to the group who were trained with the non-humanlike gripper.

Research suggests that cortical connections mediating motor activation are formed through experience [66], making perception-action coupling an important functional factor in the learning of new motor skills [67]. Under this view, a procedural memory of motor programs together with their sensory concomitants is stored during motor learning which gives rise to anticipatory mechanisms that predict sensorimotor outcomes of planned actions in real time Brain-Computer Interface and Motor Imagery Training: The Role of Visual Feedback and Embodiment http://dx.doi.org/10.5772/intechopen.78695 81

**Figure 2.** Effect of embodiment on motor imagery learning. (A) Two groups of subjects practiced motor imagery task while receiving visual feedback from a humanlike android robot and a pair of metallic gripper. (B) Subjects who were trained with the android robot demonstrated a significantly more robust learning of the motor imagery task compared to the group who were trained with the non-humanlike gripper.

an incongruent condition. Leeb et al. also compared the influence of feedback types on the motor imagery performance and BCI classification accuracy. They found that an immersive feedback (walking inside a VR environment) resulted in a better task performance by the subjects than a simple BCI feedback (bar presented on a computer screen), although this did

**Figure 1.** Users controlled a pair of humanlike robotic hands by performing right- and left-hand imageries while

The results obtained from the above studies are all consistent with our previously reported findings in [8] where subjects practiced motor imagery task in a BCI-control session with two types of feedback (**Figure 2A**). As mentioned earlier in this chapter, subjects who were trained with a more humanlike android robot could perform better on the motor imagery task in the final BCI-control session than those who were trained with a pair of metallic gripper (**Figure 2B**). In this study, "motor imagery performance" was defined as how well subjects could generate discriminant brain patterns for the two classes of right and left motor imageries and it was obtained by the Fisher's discriminant criterion in a linear discriminant analysis that observed the distribution of EEG features [8]. The ΔMotor imagery performance in **Figure 2B** represents the ratio of this criterion between the two evaluations and training sessions (for more details, refer to [8]). In another study, we also reported that in comparison with a classical feedback bar, motor imagery training with a humanlike android feedback that induces a sense of embodiment could lead to a stronger mu suppression in the sensorimotor areas and eventually improved subjects' online BCI

Research suggests that cortical connections mediating motor activation are formed through experience [66], making perception-action coupling an important functional factor in the learning of new motor skills [67]. Under this view, a procedural memory of motor programs together with their sensory concomitants is stored during motor learning which gives rise to anticipatory mechanisms that predict sensorimotor outcomes of planned actions in real time

not seem to affect the BCI classification accuracy [64].

watching first-person perspective images of the robot's body.

80 Evolving BCI Therapy - Engaging Brain State Dynamics

performance [65].

[11]. The usage of a humanlike android in our studies could have influenced motor imagery learning twofold. First, it is speculated that the visual feedback provided from the android's body resembled a self-body action—as we experience it in our daily activities—and therefore matched with the visual anticipations of the motor intentions. Second, a more detailed and compatible visual feedback from the android's body (in terms of appearance and motion) could have excited motor memories more intensely, and therefore subjects trained with a humanlike android recalled more vivid and explicit images of the movement during the imagery task [8].

Not only that embodiment can reinforce learning of the motor imagery and BCI task, it has also been shown that the two share spectral and anatomical mechanisms [68]. In the study of [68], subjects watched either a pair of virtual arms or a pair of non-body objects projecting out from their body inside a head-mounted display. For both visual feedbacks, they first received a visuotactile stimulation to experience a body ownership illusion similar to rubber hand illusion (RHI) [69], and then they were instructed to perform a motor imagery for either their right or left hand. Their overall results demonstrated that both illusory hand ownership and motor imagery were associated with a mu-band modulation, and more importantly, there was an overlap between the areas that were activated during illusory hand ownership and motor 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.
