## 10 Conclusions  
This survey delves into the various aspects and challenges of using EEG signals in human biometric authentication systems, particularly within the 5G and IoT environments. EEG-enabled IoT solutions hold the promise of transforming how brain activity is measured and monitored. Advances in sensor technology are increasingly contributing to the diagnosis and monitoring of neurological disorders, offering insights into everyday brain functions. Furthermore, these solutions can aid in developing preventative measures for mental health, providing information on cognitive patterns and behavioral responses that affect well-being. Looking ahead, EEG-enabled IoT solutions might be used to detect early changes in brain activity that precede symptoms of neurological or psychological conditions, offering valuable insights for healthcare professionals to enhance patient treatment.  
The objective of this survey is to elucidate the effective implementation of EEG biometric authentication systems within the 5G IoT architecture and infrastructure. It explores factors influencing the use of EEG signals in biometric authentication systems, including signal morphology and specific considerations for deploying EEG in 5G IoT environments. The survey also addresses common methods for EEG signal acquisition, with a focus on wearable and integrated devices, which are prominent in both IoT research and industry. Additionally, it reviews commonly used EEG databases for raw signal data, preprocessing and feature extraction techniques, the 5G IoT architecture, and the opportunities and challenges involved in EEG deployment.  
Overall, integrating EEG technology into the IoT landscape opens up numerous possibilities in healthcare, wellness, and lifestyle applications. This integration can significantly improve the efficacy of EEG technology in various healthcare contexts. However, addressing security concerns is essential when employing this technology. Developers and manufacturers must ensure product security, proper data handling, and encryption, along with implementing robust authentication measures. Users should also be cautious in handling and storing their EEG devices, remaining vigilant against potential threats posed by malicious actors. With careful consideration and preventive strategies, the risks associated with EEG-enabled IoT solutions can be effectively managed, unlocking their full potential.  
Conceptualization, T.B. and N.M.; methodology, A.R. and N.M.; software, A.R. and S.A.; validation, A.S.K., S.A., and T.B.; formal analysis, A.R. and N.M.; investigation, N.M. and A.S.K.; resources, S.A. and T.B.; writing--original draft preparation, T.B. and N.M.; writing--review and editing, N.M. and A.S.K.; visualization, T.B.; supervision, S.A.; project administration, S.A. and T.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflicts of interest. Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} IoT & Internet of Things \\ EEG & electroencephalography \\ BCI & brain-computer interfacing \\ LANs & local area networks \\ IP & internet protocol \\ fMRI & functional magnetic resonance imaging \\ SVM & support vector machine \\ HA-HV & high arousal-high valence \\ HA-LV & high arousal-low valence \\ SAM & self-assessment Manikin \\ HR & heart rate \\ GSR & galvanic skin response \\ \(\upmu\)V & microvolts \\ PCA & principal component analysis \\ APIs & application programming interfaces \\ EDI & electronic data interchange \\ FTP & file transfer protocol \\ RAN & radio access network \\ NG-RAN & new-generation radio access network \\ ng-eNBs & next-generation evolved node B \\ gNBs & gigabit network base station \\ ANS & autonomic nervous system \\ \end{tabular}