## 2 Materials and Methods  
To conduct this research, a comprehensive literature survey was undertaken, utilizing the most relevant academic platforms. Key resources were meticulously sourced from esteemed databases such as Google Scholar [29], Scopus [30], and Web Of Science [31]. The adopted approach ensures a diverse selection of articles and books, focusing on the latest and most significant research in the field. The integrity of the analysis is supported by the selection of sources from reliable platforms.  
Before undertaking all the research, it is relevant to analyze the annual growth in the number of publications, among other factors, since this usually reflects the growing relevance of the selected topics in society. The main purpose is to support the review and future research proposed at the end of the article. Figure 3 shows the increase in publications across topic clusters since 2013. The graph presents a detailed comparison of growth across the following four research clusters: reinforcement learning with collaborative robotics; reinforcement learning with EEG; reinforcement learning combined with both collaborative robotics and virtual reality; and a triad of reinforcement learning, EEG, and collaborative robotics. Each cluster's trajectory highlights the increasing significance of these integrated research areas over time.  
To ensure a systematic and rigorous review of the literature, the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology was followed [32]. Initially, approximately 500 papers were identified through a comprehensive search using specific keywords such as \"reinforcement learning\", \"collaborative robotics\", \"EEG\", and \"virtual reality\", among others. The search was conducted in the titles, abstracts, and keywords of the papers in the selected databases.  
Subsequently, a preliminary selection was made, reducing the number to about 100 papers based on the relevance of their abstracts concerning the intersection of the following four main topics: reinforcement learning, collaborative robotics, EEG, and virtual reality. Finally, a thorough and detailed reading of these 100 papers was performed, evaluating the direct and significant contribution of each one to the intersection of the mentioned topics.  
As a result, 40 valuable papers were selected, which are discussed throughout this paper and analyzed in the table presented in the Discussion section.  
The intersection of robotics with other cutting-edge research areas has experienced a pronounced growth trend beginning in 2019. This trend is especially prominent when considering the combination of robotics with reinforcement learning and virtual reality, as well as the integration of reinforcement learning with EEG technologies. The growing scholarly concentration on these subjects is representative of their increasing impact on the evolution of intelligent systems and on the progression of our understanding in the realm of human-computer interactions.  
This documented growth trajectory, consistent across all topic clusters since 2019, suggests an expanding scholarly and developmental interest, reflecting the potential that these interdisciplinary studies hold. The aim of this article is to highlight the continuous and remarkable development in these fields, emphasizing the relevance of this comprehensive review in identifying key studies. In the concluding part of this paper, key directions are outlined, providing actionable steps to foster the integration of these dynamic fields, with the aim of amplifying their collective contribution to future innovations.