## 1 Introduction  
A Brain Computer Interface (BCI) is a system that takes a biosignal, measured from a person, and predicts (in real-time) certain aspects of the person's cognitive state [1; 2]. At the outset, BCIs started as assistive technological solutions for individuals with significant speech anomalies. However, the research was rooted in a subject's desire to communicate through either speech or writing or to control his or her immediate environment. BCI systems have also used computer-based recreational activities to stimulate a subject's innate ability to overcome physical disabilities. Today, BCI-based research has been expanded to include people with and without physical disabilities. The entire system underscores how adaptive systems can enhance analytic methods and application areas. This assistive ability has created a widespread awareness among potential users and researchers alike. In the past 15 years, the increasing numbers of BCI research groups, peer-reviewedjournals, conference abstracts, and attendees at relevant conferences are indicators of the rapid growth of interest in this field. Aside from these indicators, numerous companies are collaborating with research groups to develop BCI-related technologies. These companies have further defined clear roadmaps for BCI-related technologies. There are a number of annual conferences in human-computer interaction (HCI) and BCI fields, which bring together prominent researchers to present their research projects, such as the ACM Conference on Human Factors in Computing Systems (CHI), the ACM International Conference on Multimodal Interaction (ICMI), the International Conference on Intelligent User Interfaces (IUI), Computer Supported Cooperative Work (CSCW), and the IEEE/ACM International Conference on Computer-Aided Design (ICCAD). These conferences hold many Workshops, case studies, and courses that are conducted by industry experts, practitioners, and researchers. The phenomenal growth of BCI research is aligned with an influx of researchers from diverse disciplines, including clinical neurology and neurosurgery, rehabilitative engineering, neurobiology, engineering, psychology, computer science, mathematics, medical physics, and biomedical engineering. The interdisciplinary nature of BCI research has resulted in the development of BCI systems with different target applications. Research on BCIs has shown that brain activity can be used as an active or passive control modality [3]. In an active BCI, the user controls a device using brain signals and patterns through a direct and conscious generation of commands that are wired to external applications. In contrast, passive BCIs are systems wherein brain signals yield outputs without any voluntary control. Emotional states, such as levels of meditation, engagement, frustration, excitement, and stress, are examples of affective and cognitive feedback in passive BCIs. The concept of passive BCI has been applied in various fields, such as affective computing, which aims to improve the communication between individuals and machines by recognizing human emotions, and to develop applications that adapt to changes in user state, and thereby enrich the interaction. The entire communication process enriches the interaction, leading to a natural and effective user experience. Motivated by a new understanding of brain functions and advances in computer interface devices, countless research studies on emotion detection in real-time procedures for patients and clinicians have been undertaken (e.g., [4; 5; 6]). Furthermore, similar advancements are under development for additional cognitive mental states, such as attention and workload. These cognitive mental states correspond to affective states. These and many other advances in BCI technologies have piqued scientific interest in BCI technology and its application in different contexts. The volume of studies, research, and publications on BCI emotion-based recognition systems has surged in recent years.