BCI Technology Based on EEG Signals: Principles, Applications, and Future Challenges

Electroencephalography (EEG)-based brain-computer interface (BCI) technology has garnered significant attention in recent years due to its broad potential across multiple domains, including neuroscience research, medical diagnostics, and assistive communication devices. This paper presents a comprehensive review of the fundamental principles underlying EEG-BCI systems, their diverse applications, and the prevailing challenges that impede widespread adoption. It examines key signal processing techniques employed to interpret neural signals, surveys available hardware and software platforms, and highlights clinical implementations such as epilepsy detection, motor rehabilitation, and cognitive state monitoring. Despite considerable advancements, several issues remain unresolved, including the inherent low signal-to-noise ratio of EEG signals, significant individual variability among users, and constraints related to real-time processing and system adaptability. Looking forward, future research directions emphasize the integration of artificial intelligence and machine learning for improved signal classification, enhancing the interpretability and robustness of models, and advancing the development of low-cost, portable, and user-friendly systems. This study aims to provide a thorough overview of the current state of EEG-BCI technology and to facilitate its translation from experimental laboratory settings to practical, real-world applications.