EEG-Based Brain–Computer Interface: Fundamentals, Methods, Applications, and Challenges

The Electroencephalogram (EEG)-based Brain-Computer Interface (BCI) is important for Internet of Things (IoT) applications. EEG data can be used to control IoT devices for applications such as smart home automation or healthcare monitoring. EEG-based BCI systems are crucial for recognizing human brain thoughts and analyzing neurological diseases, enabling thought visualization, and improving accessibility for people with disabilities. With the rapid development of machine learning, including deep learning technologies, wearable BCI devices, and hybrid BCI research have advanced significantly, showcasing their remarkable advantages. Researchers have conducted extensive experiments to improve the accuracy of the system. This paper provides a comprehensive review of BCI based on EEG, highlighting the fundamental principles of EEG signals, common acquisition devices, feature extraction techniques, and classification models, with a particular focus on the latest advances in deep learning. We also summarize available datasets and discuss the latest applications of EEG-based BCI in human-computer interaction and neurological diseases. Finally, we highlight the main findings and explore future directions, offering researchers deeper insight to foster further progress in this field.