This review explores the current trends and future prospects of EEG-based biometrics in 5G-IoT systems. EEG, known for its sensitivity, cost-effectiveness, and distinctiveness, is increasingly used in various fields, including neuroscience and neuromarketing. The integration of 5G networks with IoT enhances data transmission speeds and reduces latency, enabling real-time EEG data processing and analysis. This review discusses the challenges and future directions in EEG data acquisition, processing, and classification, emphasizing the role of data-driven methods in 5G-enabled IoT solutions. It also presents a case study on EEG-based emotion recognition, highlighting EEG's potential as a biometric tool in IoT applications. The review covers the fundamental properties of EEG signals, common issues in EEG data, and signal acquisition methods. It discusses the use of EEG in biometric authentication, its advantages in security and personalization, and the challenges in signal variability and user cooperation. The integration of EEG with IoT and 5G technology is shown to enhance data collection, processing, and application, leading to deeper insights into brain function and innovative solutions in healthcare and technology. The review also addresses the use of EEG in emotion recognition, the importance of EEG databases for research, and the preprocessing and feature extraction techniques used in EEG signal analysis. The study highlights the potential of EEG-based biometrics in improving security and convenience in various applications, and the ongoing research to enhance the accuracy and reliability of EEG-based systems.This review explores the current trends and future prospects of EEG-based biometrics in 5G-IoT systems. EEG, known for its sensitivity, cost-effectiveness, and distinctiveness, is increasingly used in various fields, including neuroscience and neuromarketing. The integration of 5G networks with IoT enhances data transmission speeds and reduces latency, enabling real-time EEG data processing and analysis. This review discusses the challenges and future directions in EEG data acquisition, processing, and classification, emphasizing the role of data-driven methods in 5G-enabled IoT solutions. It also presents a case study on EEG-based emotion recognition, highlighting EEG's potential as a biometric tool in IoT applications. The review covers the fundamental properties of EEG signals, common issues in EEG data, and signal acquisition methods. It discusses the use of EEG in biometric authentication, its advantages in security and personalization, and the challenges in signal variability and user cooperation. The integration of EEG with IoT and 5G technology is shown to enhance data collection, processing, and application, leading to deeper insights into brain function and innovative solutions in healthcare and technology. The review also addresses the use of EEG in emotion recognition, the importance of EEG databases for research, and the preprocessing and feature extraction techniques used in EEG signal analysis. The study highlights the potential of EEG-based biometrics in improving security and convenience in various applications, and the ongoing research to enhance the accuracy and reliability of EEG-based systems.