# Connecting the Brain with Augmented Reality: A Systematic Review of BCI-AR Systems
## 5 Discussion  
The present systematic review analyzed research articles that use EEG signals in order to control an AR environment projected on HMDs. The study relies on the results obtained from three established scientific databases: IEEE Xplore, Scopus, and PubMed. The initial section of the review showcases the statistical outcomes of the articles, including the publication year, number of participants, and BCI paradigm. In the following section, the articles are grouped into three categories according to their BCI paradigm (reactive, passive, or active). For each category, a summary of each article is presented, along with an analysis of the number of system commands, feature extraction stage, classification techniques, and evaluation metrics employed.  
The review yields numerous significant observations. The vast majority of the researchers (78.04% of the studies included) chose the reactive BCI paradigm. This may be attributed to the nature of reactive BCI techniques, such as SSVEP, which demand minimal to no training, making them well-suited for BCI-AR applications. Also, the literature showed that reactive BCI systems, and more specifically, SSVEP systems, provide the best accuracy and ITR. Another observation is the use of EEG caps instead of headbands. Since AR technology requires HMDs to render the environment, most commercial headbands are not a good solution because of their shape and size, which do not allow them to integrate with HMDs. Furthermore, the average number of participants was 12.14, which is a relatively small number. This phenomenon could have several explanations. First, most studies were conducted after 2019, coinciding with the COVID-19 pandemic, which made it challenging to gather volunteers. In addition, the physical discomfort of the systems plays a significant role in the limited number of volunteers since they need to wear both the BCI device and the HMD. Moreover, because the BCI-AR technology is still in its early stages of development, researchers primarily focus on the feasibility of the systems.  
Regarding the classification process, the researchers utilized several algorithms (Figure 6). It is worth mentioning again that the researchers were aiming to enhance the performance of their systems; therefore, in the majority of the studies, multiple algorithms were employed. SVM, alongside CCA and its variations, was the most utilized algorithms, appearing in 11 studies. LDA was another prevalent choice, being utilized in 9 studies. Moreover, Neural Networks and KNN were employed in 5 studies.  
Among the feature extraction methods (Figure 7), CCA was the most frequently used method to extract features for the classifier, being utilized in six studies. FFT ranked as the second most popular choice among researchers, being applied in five studies.  
Figure 8 shows the number of system commands for BCI-AR systems from the literature. Most of the authors designed their systems with up to eight commands. Since most  
Figure 6: Classification algorithms employed in the studies.  
of the studies utilized the reactive BCI paradigm, the system commands were displayed in the HMD. Consequently, as the number of commands increased, the user's field of view decreased, resulting in a limited view of the environment for the user. Although the mean number of commands is 7.37, the median is much lower, being 4, since two studies [21; 41] employed 36 commands for their systems. In summary, for this dataset, the median is a more representative measure of central tendency than the mean due to the presence of outliers.