**4.1 What are the factors that researchers should be focusing on when designing a gamified experiment**

## *4.1.1 Metrics for MI-BCI game mode*

*Quantitative metrics*: Researchers usually identify the learning outcome by comparing the accuracy of each experiment in sequence [43]. The testable and quantitative characteristics keep accuracy as the top used metric. Performance score, or game score/rate, is another commonly used quantitative metric equally ranked with consuming time. The game score could reflect real-time performance intra-trials and quantitative improvement cross trials objectively. One study [37] uses online game scores to motivate BCI users to improve their next performance. Moreover, line graphs for describing performance score fluctuation could be more visible for presenting learning outcomes [55]. The same Auckland group used the score-line curve one year later in another research with ADHD patients [20].

Based on the writers' experience in the rehabilitation equipment market, the scoring method is more intuitive for patients and therapists to identify the rehabilitation outcome. In contrast, accuracy is more reliable for the technical measurement of outcomes from both users and the system. Therefore, we suggest using accuracy for research aims, such as identifying whether gamification could enhance the learning outcome more than non-game MI-BCI. Synchronized gaming score, in contrast, could be applied to potentially enhance the performance of users or check the rehabilitation stage.

*Qualitative metrics*: The commonly used qualitative metric is the game experience questionnaire (GEQ ) (2/4). GEQ is the response from MI-BCI gamers. Researchers use GEQ for measuring several experiences, such as flow experience. A guideline is recommended to quantify all the qualitative answers and group them into a specific experience [57]. For example, Tezza et al. [26] use a 1–5 Likert scale to evaluate the experience.

#### *4.1.2 Existing issues*

*Environmental Background and Distraction*: Distraction is an environmental or feedback issue that would disturb the users from performing a correct MI. In one of the earliest MI-BCI gaming experiments [24], researchers assumed distraction caused the no-growth low mu rhythm used for game control. In a simplified football game [47], 7 out of 20 subjects argue similar disturbing feedback. One article reports the same disturbing issue [35], but they claim this issue offers more engagement in the game, in contrast. Serious consideration to balance the augmented engagement and distraction, thus, is required when applying the idea of multi-player MI-BCI games.

*Trial Length, Experimental Time and Fatigue*: Fatigue is another issue that generally happens during MI-BCI training. The length of trials and the time for experiments are linked to fatigue. Significant variability in performance is found in different lengths of trials [23], which might be due to fatigue. In one 2D B2B game [2], researchers discover similar performance variability due to evident fatigue and depression crosssubjects. In detail, two participants complain that the sessions are too long to keep them performing well. Researchers report similar fatigue issues and lack of concentration in one subject whose performance is not ideal after four sessions [36].

Authors from the 2D B2B game research [2] also believe that the chosen time (afternoon) for an experiment is one factor that increases the tiredness providing low performance. One puzzle game case study [55] supports this argument, in which authors suggest a morning experiment to avoid fatigue.

*Performance Variation*: Performance variation is when outcomes of distinct trials are different. It often happens when different individuals use the same MI-BCI device with all other factors being the same. For example, in one gamified training mode [27], two participants showed improvement in the game performance while one subject showed a decrease in performance.

However, variation would not only be found in cross-subjects but also found in the same subject when they failed to provide reproducible MI effort in each trial. This variation is called intra-subject performance variation. Psychological states, such as nervousness and motivation, correspond with intra-subject performance variation [40]. Another study [31] reports the same issue and claims that it is possible to solve this problem by using adaptive assistance in changing the trial length. Consistency and personalization are two issues causing cross-subject and intra-subject performance variability [42]. Researchers predict that a competitive training environment might help maintain users' excitement [35]. However, this reviewer would consider whether this environment could have side effects, such as distraction, on learning outcomes in a short time.

*The Increasing False Positive Rate*: The false-positive rate is the proportion when the classified command is against the imagination of participants. Scherer et al. [27] reveal that the increasing false positive rate might be related to premature brain activation. Researchers also believe this non-stationary brain wave links with the unsatisfactory robustness of the system.

An optimal threshold is also a factor that impacts the false positive rate. The threshold of MI-BCI is the digital criteria to identify whether the amplitude of Electroencephalogram (EEG, one type of brain signal) could be a task-relevant mental task. One study shows the rate declined when adjusting the decision boundary from 0.6 to 0.8 [44].

*Performance decrease*: Performance decrease is the declining quality of quantitative performance metrics, such as decreased accuracy, ITR, and increased time. One study *Improving the Brain-Computer Interface Learning Process with Gamification in Motor Imagery:… DOI: http://dx.doi.org/10.5772/intechopen.105715*

indicates its link with increasing workload [45]. To solve this, reducing the physical demand is recommended [53]. Furthermore, to keep a good performance, minimum eyes and body movement are required [44].

*Toughness in playing games*: Toughness is the difficulty for participants to finish a goal in the game. One study [42] argues that though the gaming concentration is satisfied, the controlling difficulty is still visibly raised when the number of MI classes grows. This evidence informs that the number of MI control types impacts toughness, which could then cause performance variability.

## **4.2 What games have been used in MI based BCI?**

#### *4.2.1 Feasibility of MI-BCI gaminification*

Appendix 3 indicates the evidence of the feasibility analysis. For research reporting with performance accuracy, the criterion of feasibility is that the average accuracy (accuracy/no. person) could finally reach 70% of accuracy, based on the criteria of BCI application [58, 59]. Although the articles concluded that 233 subjects successfully went through the MI-BCI experiments, only 111 individuals have examined the feasibility of the MI-BCI game with its gaming accuracy.

This review chooses the maximum average accuracy (MAA) for quantified feasibility as one metric. For example, Djamal et al. [34] presented two groups of average accuracy (a non-FFT group with 50% versus an FFT group with 70%). This review picked the 70% FFT group as an MAA since this accuracy is reachable with existing system upgrade technologies, such as FFT. After analysis, 28 chosen articles present an average accuracy of 74.35%, higher than the threshold. Additionally, 26 out of 28 articles (92.86%) possess positive responses to the feasibility of the MI-BCI game. These two results illustrate that it could be feasible to apply gamified MI-BCI training mode for further experiment.

## *4.2.2 Game recommendation*

From the analysis above, it is explicit that 2D AO is the commonly used game type (5/28, 17.86%). The evidence to support this argument is generally high (46.5 total scores in AQ with a mean of 9 and a standard deviation of 2.17). These statistic results advise 2D AO to become a high-priority recommended game. The advantage of the 2D AO game is the high level of motivation. The coming obstacles would increase the tension and stimulate the users to accomplish the correct MI to rescue the character intuitively.

However, except one study uses 3 MI classes (left- and right- hand MI and feet MI), others do not use more than two MI types for gaming navigation. This circumstance is a limitation of current 2D AO games. Future 2D AO game design should cater to real-world complexity by providing more MI class on the one hand and a reliable user experience with guaranteed accuracy on the other hand.

A potential solution is to add more controllable gaming factors in the 2D AO game. For example, forward and backward, acceleration and deceleration, and even shooting a bullet against the obstacle could become controllable by additional MI types more than just left and right hands' imagination. Furthermore, different MI classes could depend on environmental color changes. 3D CyR games apply this idea to reality [40–42], where competitors produce four MI classes based on the above mission colors.

2D B2B game is ranked equally with 3D CyR in report number (3/28, 10.71%) and similar quality (30.5 total scores in 3D CyR vs. 29.5 in 2D B2B). Like a 2D AO game, game designers should notice the limited MI control classes since all three studies using 2D B2B games only present the learning outcome of two classes of MI (Left and Right hand). The failure in recommending a 2D B2B game is because of its lower level of motivation than a 2D AO game [35].

We advise the 3D CyR game since this game could be not only for training but also for the Cybathlon competition. These applications encourage researchers to further the development of rehabilitation and patients to open out to build relationships and share their life with their competitors from other nations or territories. Nonetheless, an advanced challenge for the CyR game requires an excellent feature selection method to afford the upgraded complex data calibration and classification due to the increasing number of MI classes and highly competitive interruptions.

One limitation in all three games above is the lack of immersion, i.e., players feel difficulty linking their MI with their motor execution (ME) when playing a game. This condition raises challenges for presenting correct MI since no environment could work as humanoid neurofeedback to motivate the gamer to produce the related MI. Therefore, humanoid factors should be a focus when utilizing these three types of games.

First-person VR action games (VR FP) do not need to consider humanoid environment issues. Advantages of VR application are imaginative immersion and a high level of sensation. [56] Karácsony et al. [44] share the comments from participants who played VR B2B games: gamers feel they are using their own hands to catch the falling balls via VR-based MI-BCI game. Therefore, these humanoid advantages could improve the participants' performance [44]. Furthermore, one study reports a high gaming accuracy in the VR B2B game. However, the VR DR game does not reproduce a similar extent of learning outcome. This review believes a lower level of immersion, such as a non-first-person humanoid control environment and no visible link between MI and navigation, causes the game to become relatively distracting. This phenomenon prevents the VR DR game from recommendation.

The interest and the difficulty of the game design are required to consider [27]. Although VR games could reach a high level of learning outcome, the complexity of designing the platform and synchronized interaction with numerous MI-BCI data is not as easy as a 2D PC game. The immersion provided by a VR game might be lower than a 2D AO game if designers sacrifice the quality of a game system to reduce complexity. That is because potential high delay, game bugs, and illogical graphical switches might occur as distractions reducing the MI-BCI experience. A 2D MI-BCI game with good design could also present well in tactical, strategy, or narrative immersion, which are not specific to VR [30]. These facts prevent VR FP action games from becoming the only recommended game.

In summary, if there are temporal and spatial limitations, we advise researchers to choose game content close to 2D avoiding obstacles. The insufficient degree of immersion and the number of MI classes are two main issues that need to consider. 3D Cybathlon running game is another choice for researchers due to its futuristic maturity in competition, stakeholders, and technique. In contrast, we recommend a VR first-person action game with first-person vision when there is no limitation in designing and experimenting with an upgraded gamified MI training system. In particular, the VR ball to the basket game and destroying asteroids game are suggested as two existing first-person VR examples.

Another game genre, puzzle game, is not recommended in this review because motor imagery is still easily disturbed by other factors, as mentioned in both current *Improving the Brain-Computer Interface Learning Process with Gamification in Motor Imagery:… DOI: http://dx.doi.org/10.5772/intechopen.105715*

issues and Appendix 2. A hybrid game with an action factor to motivate correct MI and a puzzle factor to enhance the strategy immersion is still potential for improving learning outcomes of MI-BCI. However, we suggest designing the game navigated by action-dominated factors. For example, the game story could cover puzzle game questions. However, for choosing an answer and continuing the story, the graphical interface of the MI-BCI game could use a humanoid hand for assistance.

### **4.3 Limitation**

No article has discussed the comparison between gaming MI-BCI with a control group. The main reason is that majority of the studies are still focusing on the feasibility of using different feature selection methods in the MI-BCI game, aiming to improve the performance outcome with more advanced techniques. Even though three existing articles [32, 43, 47] claimed the improved learning outcome with comparison tests, their research process is not sufficiently scientific as randomized control trial (RCT). This limitation shows a research gap that could be filled shortly: a randomized control experiment for evaluating whether gamification could improve the learning outcome of MI-BCI is required.

Another limitation is the objectiveness of this review. Since gamification in MI-BCI is an interdisciplinary study covering tremendous academic fields, this review cannot present an in-depth analyzed review and recommendation. This research group hopes to complete a systematic review with video game experts when enough RCTs have tested an optimal training mode.

Furthermore, we advocate more significant attention to users' cohorts. Only 7 out of 28 articles test the feasibility of games in a disabled cohort. One study in this group using a commercial wireless BCI device reports a highly negative result: all participants failed to complete the initially designed trial. None of them shows a learning outcome increase after the full training [55]. This situation absorbs attention on an appropriate task for different users. A relatively short training duration with a more comfortable experiment setting is probably more friendly to patient-participants. Additionally, one study reports the possibility that subjects with video game experience would have a better MI-BCI performance [60]. Gender is also a probable factor related to performance [46]. Therefore, when separating the participants and analyzing the results, these participants-relevant factors should consider.

Real-World Study (RWS) [61] is a research type covering the data collected widely and randomly. Researchers pick the evidence (Real World Evidence) without strictly classifying one controlled particular pattern. For MI-BCI, existing data is often gained from healthy cohorts or individuals with specific neurological impairments. These data are not sufficient for testing the robustness and reliability of MI-BCI for real-world users and applications. More influential factors would occur than subjects whose patterns are controlled in an RCT. For applying MI-BCI to these real-world users with a reliable outcome, this review recommends having an additional RWS after a sufficient number of RCT studies show positive results.

#### **4.4 Insisting pure MI-BCI control strategy in game**

This review studied gaming BCI systems controlled by only MI. Plenty of research has tested the feasibility of hybrid BCI control [62] with games. One article successfully used a hybrid technique with MI to control a well-known but highly complex game, World of Warcraft, surprisingly seven years before [63]. Although these combination control

methods might accomplish more remarkable improvement than mere MI control, this review still insists on the importance of exploring the maximum outcome of pure MI.

The reason for insisting on single MI control is that the source of performance issues still needs to be researched. On the one hand, well-direct optimization of the pure MI control could efficiently improve the hybrid control system. This review is not denying the better performance that hybrid MI devices could achieve. In contrast, after digging out and solving the problem caused by MI-related factors, a combination of other methods might have higher performance growth in another dimension.

On the other hand, even if a hybrid MI system has been designed, the outcome would still not be ideal if we have not sufficiently explored and solved the potential performing issue. Like a disease, low MI performance is a symptom of MI-BCI, and unsuitable MI design is one of the root causes. Using merely hybrid MI control seems like treating the low-performance symptom, but not the root causes. This assumption could be supported by one study [62] that participants still need to be trained for a long duration with a hybrid control system that has been applied in their Tetris game. In contrast, discovering suitable MI-BCI gamification to help users is a metaphor for treatment for the root cause.

As a product manager in Fourier Intelligence, our regular marketing survey shows that patients requiring long training duration would still get bored of the game provided by our rehabilitation equipment. However, a number of games have been included in our game library for different rehabilitation equipment. Therefore, providing MI-BCI games to patients requiring several weeks or even months of training would still challenge keeping patients engaged. Designing other MI-BCI games for satisfying patients might not be a long-term solution (**Figure 2**).

**Figure 2.** *An example of training game in game library.*

*Improving the Brain-Computer Interface Learning Process with Gamification in Motor Imagery:… DOI: http://dx.doi.org/10.5772/intechopen.105715*
