**1. Introduction**

Brain-computer interfaces (BCIs) have been considered for years as a new method of control and communication with the outside world not only for disabled patients who have lost motor control [1, 2] or speech abilities [3], but also for healthy users who are seeking new ways of interaction with virtual reality (VR) environments [4] and gaming applications [5].

Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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However, despite their popularity and potentials, BCIs still remain mostly used inside laboratories and barely commercialized for real-world applications. The main reason behind this slow progress is the lack of reliability and poor performance of the BCI systems [6]. Even the finest BCI classifiers developed to date are not yet able to extract the relevant features from brain activity with high accuracy and robustness, particularly if the activity is recorded with electroencephalography (EEG) and contains noise. Many BCI researchers have made it a quest of their life to develop systems and algorithms that can decode EEG activity with high accuracy [7]. However, beside the algorithms, there is another element in the BCI loop that often gets neglected and that is the human user who is the source of the input signals [6, 8]. Although it has been previously shown that not every user is capable of controlling a BCI, the so-called BCI illiteracy [9], most users can obtain a decent level of "skill" with a few sessions of training.

To overcome such a deficiency, some studies have employed a double-modality design. For instance, Chatterjee et al. introduced a vibrotactile feedback paradigm that delivered haptic information during BCI control [15]. Every time subjects imagined a hand movement, the classifier result was presented to them in the form of a cursor movement (visual feedback) and a vibration on their corresponding arm (tactile feedback). A design like this can enormously change the interaction a clinical BCI user has with a neuroprosthesis and may facilitate the decoding of sensorimotor rhythm during neurorehabilitation therapy with BCIs [16]; however, in the case of a healthy user, the application of vibration on a part of body that is not involved in the imagination of movement (arm instead of the hand) can again disturb the

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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Another commonly faced problem in the BCI training protocols is the lack of motivation for novice users. Motor imagery BCI takes a very long training that is often accompanied with unsuccessful and unsatisfying results in the beginning. It has been shown that motivation [17, 18] along with other mental states such as fatigue and frustration [19] can substantially influence BCI performance. To alleviate this problem, some of the previous studies have given their attention to the design of a more interactive feedback environment by means of virtual reality techniques [18, 20]. A few others have tried to improve users' level of confidence and perception of control over the BCI system by intentionally biasing the presented feedback

What is important, and often neglected in the BCI research, is that the interaction between a user and the interface is the most critical component in the BCI loop, and therefore an inappropriate training design can hinder the user's learning of the task and BCI skills. In this chapter, we address the importance of training and feedback design in the production and control of the EEG components that are required for a motor imagery-based BCI. We first review research on the compatibility of the feedback appearance with a real human body and its impact on learning of the motor imagery task. We then discuss works that have tried to improve the motivation level of a user either by making the environment playful or by positively faking the performance of the user. In the following, we investigate the role of embodiment, the feeling of owning a controlled body, which has long been disregarded in the BCI research. In the final part of this chapter, we introduce our android-based training paradigm that has exhibited a

It has been shown that mental imagery of a motor action can produce cortical activation similar to that of the same action executed [23, 24]. For instance, the execution of a hand movement results in the suppression of mu rhythm (8–12 Hz) in sensorimotor regions and so does the motor imagery of the corresponding hand [25]. By monitoring single-trial EEG signals and measuring event-related desynchronization (ERD), it is even possible to detect whether the imagined hand was the right or the left one [26]. However, previous studies suggest that the detection of hand imagery can only achieve a high rate when the user has employed a kinesthetic motor imagery strategy (first-person process) [27]. In the same study, Neuper et al.

promising potential for improving motor imagery learning in novice BCI users.

**2. Motor imagery and action observation**

conduct of the motor imagery task by the user.

accuracy [21, 22].

Motor imagery-based BCIs demand particularly longer training time compared to ERP-based BCIs (such as P300 speller) or BCIs that use steady-state visual-evoked potentials (SSVEPs). This is due to the fact that motor imagery task, the mental rehearsal of a movement without actually performing it, is a counterintuitive task for the majority of individuals. Most users cannot visualize a vivid picture of the movement and its kinesthetic experience. Hwang et al. refer to this as the unknown "feel of motor imagery" [10]. An imaginary action can range from the visualization of a self-performed movement from a first-person perspective, to a thirdperson view of the self-body movement, to the manipulation of an external object that is either physical or imaginary [11]. Although these types of motor imagery all involve voluntary actions, they may not involve similar cognitive processes. For novice BCI users, the instruction about a motor imagery task is normally given verbally by an experimenter, and it is up to the user to find the optimum image, by trial and error, that leads to a high performance.

On the other hand, similar to any other interface, BCI users should receive feedback of their performance in order to close the control loop between them and the interface. Over years, various feedback paradigms for motor imagery training have been proposed, most of which are based on visual and auditory feedback [12, 13]. One of the main issues in the design of visual feedback in most of motor imagery-based BCIs is that the feedback presentation is not congruent with the subject's image of a bodily movement. For example, in the training paradigm introduced by Pfurtscheller and Neuper, subjects imagined either a right- or a left-hand movement and watched a horizontal feedback bar on a computer screen that was extended to the right or to the left based on the classifier output [12]. Blankertz et al. presented a falling ball on the screen that could be horizontally displaced either to the left or right side if the user's left- or right-hand imagery was successfully detected by the classifier [13]. In another study, Nijboer et al. employed two feedback designs: a visual feedback with a cursor on a screen that moved up and down based on the subject's sensorimotor rhythm and an auditory feedback that presented different types of sound in existence or the absence of motor imagery activation [14]. In all of the given examples, the feedback design that was employed had no congruity with the type of image that the subjects held (image of a bodily hand or a foot). Not only the disparity between the visual feedback and the type of image can confuse the subjects during the task, but it also prevents them from obtaining "the feel of motor imagery" and correcting their imagery strategy.

To overcome such a deficiency, some studies have employed a double-modality design. For instance, Chatterjee et al. introduced a vibrotactile feedback paradigm that delivered haptic information during BCI control [15]. Every time subjects imagined a hand movement, the classifier result was presented to them in the form of a cursor movement (visual feedback) and a vibration on their corresponding arm (tactile feedback). A design like this can enormously change the interaction a clinical BCI user has with a neuroprosthesis and may facilitate the decoding of sensorimotor rhythm during neurorehabilitation therapy with BCIs [16]; however, in the case of a healthy user, the application of vibration on a part of body that is not involved in the imagination of movement (arm instead of the hand) can again disturb the conduct of the motor imagery task by the user.

Another commonly faced problem in the BCI training protocols is the lack of motivation for novice users. Motor imagery BCI takes a very long training that is often accompanied with unsuccessful and unsatisfying results in the beginning. It has been shown that motivation [17, 18] along with other mental states such as fatigue and frustration [19] can substantially influence BCI performance. To alleviate this problem, some of the previous studies have given their attention to the design of a more interactive feedback environment by means of virtual reality techniques [18, 20]. A few others have tried to improve users' level of confidence and perception of control over the BCI system by intentionally biasing the presented feedback accuracy [21, 22].

What is important, and often neglected in the BCI research, is that the interaction between a user and the interface is the most critical component in the BCI loop, and therefore an inappropriate training design can hinder the user's learning of the task and BCI skills. In this chapter, we address the importance of training and feedback design in the production and control of the EEG components that are required for a motor imagery-based BCI. We first review research on the compatibility of the feedback appearance with a real human body and its impact on learning of the motor imagery task. We then discuss works that have tried to improve the motivation level of a user either by making the environment playful or by positively faking the performance of the user. In the following, we investigate the role of embodiment, the feeling of owning a controlled body, which has long been disregarded in the BCI research. In the final part of this chapter, we introduce our android-based training paradigm that has exhibited a promising potential for improving motor imagery learning in novice BCI users.
