The article "Review of EEG-Based Biometrics in 5G-IoT: Current Trends and Future Prospects" by Taha Beyrouthy, Nour Mostafa, Ahmed Roshdy, Abdullah S. Karar, and Samer Alkork provides a comprehensive overview of the integration of electroencephalography (EEG) biometrics with 5G and IoT technologies. The authors highlight the increasing role of IoT in daily life and the enhanced capabilities of 5G networks, which have significantly altered human-machine interactions. EEG, known for its sensitivity, cost-effectiveness, and distinctiveness, is emerging as a key biometric in various fields, including neuroscience and neuromarketing. The article discusses the challenges and future directions in EEG data acquisition, processing, and classification, emphasizing the increasing reliance on data-driven methods in 5G-supported EEG-enabled IoT solutions. A case study on EEG-based emotion recognition is presented to illustrate the practical application of EEG biometrics in the IoT domain, driven by 5G technology. The review covers the fundamental properties of EEG signals, signal acquisition methods, signal processing techniques, and feature extraction and classification algorithms. It also addresses the integration of IoT and 5G technologies in EEG systems, highlighting their potential to enhance data collection, processing, and application in healthcare, research, and neurotechnology. The article concludes with a discussion on the challenges and opportunities for seamless integration of wearable devices with IoT, emphasizing the need for further research to improve data acquisition methods, signal processing algorithms, and user-friendly systems.The article "Review of EEG-Based Biometrics in 5G-IoT: Current Trends and Future Prospects" by Taha Beyrouthy, Nour Mostafa, Ahmed Roshdy, Abdullah S. Karar, and Samer Alkork provides a comprehensive overview of the integration of electroencephalography (EEG) biometrics with 5G and IoT technologies. The authors highlight the increasing role of IoT in daily life and the enhanced capabilities of 5G networks, which have significantly altered human-machine interactions. EEG, known for its sensitivity, cost-effectiveness, and distinctiveness, is emerging as a key biometric in various fields, including neuroscience and neuromarketing. The article discusses the challenges and future directions in EEG data acquisition, processing, and classification, emphasizing the increasing reliance on data-driven methods in 5G-supported EEG-enabled IoT solutions. A case study on EEG-based emotion recognition is presented to illustrate the practical application of EEG biometrics in the IoT domain, driven by 5G technology. The review covers the fundamental properties of EEG signals, signal acquisition methods, signal processing techniques, and feature extraction and classification algorithms. It also addresses the integration of IoT and 5G technologies in EEG systems, highlighting their potential to enhance data collection, processing, and application in healthcare, research, and neurotechnology. The article concludes with a discussion on the challenges and opportunities for seamless integration of wearable devices with IoT, emphasizing the need for further research to improve data acquisition methods, signal processing algorithms, and user-friendly systems.