## 1 Introduction
The growth of this market is attributed to factors such as the rising demand for biometric security systems in sectors like banking, finance, and government, and there is an increased emphasis on access control and individual  
Figure 2: Various applications of EEG biometric signals in IoT solutions. authentication in healthcare and retail. Furthermore, the use of mobile biometrics is escalating swiftly, establishing itself as a potent authentication method in virtually every mobile device and smartphone [26]. Regarding the IoT, human biometrics has emerged as a powerful technique that improves security and convenience in a variety of applications. Biometric authentication technologies such as fingerprint recognition, iris scanning, face recognition, and voice verification are being incorporated into IoT devices, allowing for safe and smooth interactions between people and connected devices [27]. When compared to traditional password-based or token-based authentication systems, these biometric-based solutions offer higher levels of accuracy and dependability. Furthermore, they provide a better user experience by eliminating the need to memorize difficult passwords or carry physical keys. IoT devices can precisely identify and verify individuals by employing biometrics, allowing for personalized experiences, secure access management, and efficient monitoring in sectors such as healthcare, smart homes, transportation, and security [28]. Incorporating EEG signals into biometric recognition systems represents a significant advancement in the field of identity authentication. While traditional biometric methods like fingerprint and facial recognition have been widely used, EEG-based biometrics offer unique advantages that, in some cases, surpass these conventional techniques. EEG-based biometric recognition utilizes the distinct and hard-to-replicate electrical activity of the brain, positioning it as a secure and innovative approach in identity verification. EEG captures the brain's electrical activity, offering deep and dynamic insights into an individual's cognitive processes. This uniqueness forms the cornerstone of its application in biometric recognition, enabling a more robust and personalized approach [29]. One of the key advantages of EEG-based biometrics is its ability to capture not only static physical attributes but also dynamic cognitive responses. This dynamic nature adds an additional layer of security, posing a challenge for malicious entities attempting to replicate or deceive the system [30]. In practice, EEG-based biometrics typically involve recording brainwave patterns through non-invasive electrodes. Advanced algorithms and machine learning techniques are then applied to analyze these patterns and extract distinctive features for individual identification. This method finds applications in secure access control, user authentication in computing systems, and forensic science. Despite its potential, EEG biometrics face challenges such as signal variability due to emotional or physiological states and the necessity for user cooperation. Future research is directed toward improving data acquisition methods, enhancing signal processing algorithms, and integrating EEG biometrics into practical, user-friendly systems [31]. Key attributes of EEG in biometrics include the inherent uniqueness of EEG patterns to individuals, shaped by their distinct neural pathways and brain activities. The dynamic nature of brain waves, which react to various stimuli, adds a layer of complexity, thereby enhancing security. Moreover, unlike fingerprints or iris patterns, EEG signals are difficult to mimic, reducing the likelihood of spoofing attacks. Standing at the forefront of secure authentication technology, EEG-based biometric recognition, with continued research and technological advancements, has the potential to revolutionize personal identification and security measures in the digital era [32; 33].