## 1 Introduction
This merging is set to improve data collection, processing, and application, leading to deeper insights into the human brain and fostering innovative solutions in both healthcare and technology sectors. As these technologies continue to evolve, their impact on EEG research and applications is expected to expand, broadening our understanding and use of brain activity data [39; 40]. The scope of the study was initially defined, encompassing key terms that are essential to the research, such as EEG, IoT, and 5G. This scope was instrumental in accurately identifying relevant aspects of the literature study. Academic databases like Elsevier, MDPI, IEEE Xplore, etc., were utilized. Keywords related to EEG, IoT, 5G, and their interrelated aspects were combined in the search strategy. The literature was thoroughly filtered based on criteria such as relevance, recency, and methodology. Priority was given to peer-reviewed articles and reliable sources, especially those focusing on the classification of EEG signals in the context of IoT and 5G integration. A thematic analysis was conducted on the chosen literature to extract and highlight key methodologies, challenges, and advancements, as reported in various studies. The primary goal was to develop a comprehensive understanding of how IoT and 5G technologies enhance EEG signal classification. Specific research questions were formulated to steer the study, including the investigation of the impact of 5G on real-time EEG monitoring and the exploration of the role of IoT in the development of interconnected EEG devices. The diagram in Figure 3 presents a logical flow and interconnection of the various sections in the review paper, indicating how each section builds upon the previous ones and contributes to the overall narrative and understanding of integrating EEG with 5G and IoT technologies. Section 2 follows the introduction, delving into a specific application of EEG technology in the realm of emotion recognition. This section discusses the progress in EEG-based emotion recognition and relevant EEG databases. Section 3 provides an exploration of the fundamental properties of EEG signals, which is essential to understanding the subsequent sections on signal acquisition and processing. Section 4 addresses the common issues and noise factors in EEG data, which are crucial for ensuring accurate data interpretation in EEG studies. The acquisition of EEG signals is detailed in Section 5, where various methods for capturing EEG signals, such as different types of sensors (e.g., electrical geodesic, cup electrodes, dry electrodes, inductive sensors, ultrasound sensors), are presented. This section is foundational to understanding how EEG data are gathered before they are processed and analyzed, while subsequent pre-processing and feature extraction are detailed in Section 6, this section focuses on the techniques used for preparing EEG data for analysis, including filtering, segmentation, channel selection, feature extraction, and classification. This section might also delve into the methodologies, applications, challenges, and future directions in EEG signals. An architectural overview of the EEG biometric deployment in 5G-enabled IoT is presented in Section 7; this section explores the integration of EEG technology with modern IoT and 5G infrastructures, discussing aspects like EEG sensing networks, IoT cloud layers (mobile and edge computing, storage, application, intelligence, connectivity and integration, security, and authentication), and graphical user interfaces. Section 8 specifically delves into the architecture and infrastructure of 5G IoT, which is likely related to the deployment of EEG technology in these environments. Finally, challenges and opportunities for the seamless integration of wearable devices with IoT are described in Section 9, where the potential and difficulties associated with wearable EEG devices and their integration into daily life or medical applications are addressed.