# Integration Between Serious Games and EEG Signals:
## 5 Discussion  
The integrated comparison of brain-computer interface (BCI) systems reveals diverse experimental strategies, development platforms, and applications, highlighting key trends and challenges. Spontaneous signal strategies allow users to voluntarily generate brain patterns through mental imagery or cognitive tasks, enabling richer interaction but often requiring extensive training. Hybrid approaches combine brain signals with physiological data for robust applications, particularly in rehabilitation scenarios. This review showed that the experimental strategy based on spontaneous signals is the most frequently used, with 50% of reported studies using it. The evoked signals and hybrid approaches are tied, with each technique being used by 25% of the studies analyzed. It could be said that spontaneous signals are preferred because they provide high degrees of freedom in association with real and imagined movements of hands, arms, feet, and tongue [74]. In addition, due to the independence of external stimuli, they are preferable in control applications and provide the user with direct control [75,76]. Evoked potential systems rely on external stimuli to elicit brain responses, offering high precision but limiting user autonomy. Development platforms like Unity dominate for their flexibility, while custom-built frameworks cater to specific experimental needs. Signal processing tools such as Matlab and Python, along with the software development kits (SDKs) of commercial EEG hardware solutions (like Emotiv and NeuroSky), facilitate real-time analysis, although integration across systems remains a challenge. According to [45], other challenges are related to accuracy, speed, price, and usability, particularly for spontaneous and hybrid systems. However, the training phase in BCI systems is another challenge faced by researchers in EEG-based games. Training both the user and the system requires long and repetitivetrials that often result in fatigue and poor performance. Additionally, many users cannot voluntarily modulate the amplitude of their brain activity to control the neuro-feedback circuit. In this context, due to the large variability of individual EEG signals, it is almost impossible to have a single universal training for different brain signals [18] and to develop \"universal\" neuro-feedback systems that can be applied to all users without the need for some time-consuming customization or individualization [77]. There are different types of games and degrees of complexity in the design, development, and implementation of serious games. Game types range from simple navigation and target selection to complex motor imagery and rehabilitation-focused games, with commands varying from directional inputs to hybrid multimodal controls. Some research has hypothesized that experienced players might have better performance when using a BCI system because they have developed visio-motor skills derived from the game [69; 78; 79]; this aspect can be taken into account when designing the experiments. The number of test subjects varies significantly, with smaller feasibility studies (less than 10 participants) and larger validation experiments (more than 25 participants) reflecting differences in research maturity. Most serious games integrated with EEG systems are still in the prototype stage, and this may be the reason for the small groups of users in the proofs of concept. Often, the number of people involved in the experiments does not correspond to a representative sample that allows for validating the system or even performing a statistical analysis. The reliability of BCI systems in real-time command operation varies by strategy and game design. A key metric often reported to evaluate reliability is the accuracy of command execution, which provides insight into the system's effectiveness in translating brain signals into actionable control. Evoked potential systems show high accuracy due to their deterministic nature, achieving 94.5% in a \"Run and Jump\" game [45] and 85-90% in object selection tasks [62].