Classification and Analysis of EEG Montage

Electroencephalography (EEG) has gained considerable traction in diverse research and clinical applications due to its non-invasive nature, real-time brain activity monitoring capabilities, and increasing affordability. This literature survey explores the applications and advancements of EEG technology in fields such as sleep studies, seizure detection, Alzheimer's disease diagnostics, user authentication, intensive care monitoring, and brain-computer interfaces (BCIs). Innovations such as reduced-channel setups and subdermal electrodes have improved EEG accessibility, while machine learning algorithms have enhanced signal interpretation and diagnostic accuracy. For instance, simplified EEG configurations show promise in seizure detection and home sleep monitoring, while TMS-EEG-based methods improve Alzheimer's diagnostics. EEG-based user authentication methods present a unique approach to secure identification, though privacy remains a concern. Additionally, EEG-driven BCIs are evolving, offering new ways for individuals with motor impairments to interact with technology. This review highlights EEG's transformative potential across these domains, identifies current limitations, and suggests future research directions, particularly in enhancing signal quality, data privacy, and diagnostic accuracy.