## 1 Introduction
EEG data--known for their stability, universality, and distinctiveness--have broadened their applications from conventional brain-computer interface (BCI) solutions to encompass more varied fields, including neuroscience and neuromarketing. Figure 2 illustrates the primary emerging markets where EEG biometrics find applications within the context of the IoT [8]. BCI technology encompasses a broad spectrum of applications. A prominent example is BCI-controlled prosthetics, which enable individuals to use their thoughts to control devices aiding in daily activities. These devices range from wheelchairs to exoskeletons, designed to improve mobility for those with motor disabilities. Additionally, BCI technology shows promise in therapeutic domains. Current research is exploring the use of BCIs for monitoring, understanding, and potentially addressing neurological disorders like  
Figure 1: Global mobile data traffic forecast for the 2020 to 2030 period (ITU). depression and anxiety. Another significant area of BCI applications is health monitoring. Analyzing brainwaves through BCIs can detect shifts in mental states, such as fatigue, alertness, or levels of intoxication [9; 10; 11; 12; 13]. Technology can also function as an instrument for identifying individuals with certain neurological or psychological conditions. For example, EEG, a component of BCI, can be employed in the diagnosis of epilepsy and other neurological disorders, including Alzheimer's disease and autism spectrum disorder [14; 15; 16; 17]. Additionally, it can aid in the diagnosis of mood disorders like depression and anxiety [18]. EEG can also help identify individuals who may be experiencing sleep problems, such as insomnia [19]. BCI systems utilizing EEG initially enabled the movement of a cursor on a computer screen or the selection between two images using brain signals. A key advantage of EEG signals is their rich content, reflecting a person's mental state, cognitive and motor functions, and a range of other neurological activities [9; 20]. Therefore, EEG signals are particularly well-suited as input sources for BCI systems. EEG can be employed for individual identification based on prior training. Researchers have explored the potential of using a person's EEG as a biometric identifier by analyzing the unique properties and patterns of their EEG signals [21]. Beyond its traditional uses, EEG has found applications in tailored scenarios like yoga classes, meditation sessions, immersive gaming experiences, and virtual reality platforms [22]. ts widespread use in understanding human brain functionality extends to gaining insights into human thoughts, needs, and emotions, particularly in marketing and strategic planning. EEG is increasingly being used to gauge customer reactions to marketing messages, advertisements, and product designs. In the realm of marketing, EEG provides valuable insights into customer perceptions, emotions, and responses to various stimuli. It can reveal hidden consumer preferences and guide the development of effective marketing strategies, messages, and product designs [23]. EEG fusion, a novel technological advancement, merges the capabilities of EEG, known for detecting brainwave patterns, with the IoT. This approach involves linking sensors and devices to the internet and leveraging these connections for the analysis and interpretation of brain signals. This technology is increasingly being used to create applications focused on monitoring, diagnosing, and treating mental illnesses, improving cognitive functions, and promoting overall health and wellness [24]. The biometric technology market is anticipated to expand from USD 21.5 billion in 2019 to USD 64.8 billion by 2025, demonstrating a compound annual growth rate (CAGR) of 22.0% during the forecast period [25].