Alzheimer’s disease stands as a significant global health challenge, with new effective diagnostic and therapeutic interventions being. Among emerging technologies, Brain-Computer Interface (BCI) systems utilizing electroencephalography (EEG) have shown promise in detecting and monitoring neurodegenerative disorders such as Alzheimer’s. This research paper presents a comprehensive review of recent advancements and applications of EEG-based BCI in Alzheimer’s disease research. The review encompasses studies investigating EEG biomarkers, signal processing techniques, machine learning algorithms, and BCI paradigms for early detection, disease progression tracking, and further controlling the disease to progress in the latent stage and cognitive assessment in AD patients. Additionally, the paper discusses the challenges encountered in EEG-based BCI research for Alzheimer’s, including signal noise, inter-subject variability, and limited data availability. Furthermore, it proposes future directions and potential strategies to overcome these challenges, such as multi-modal integration, novel feature extraction methods, and collaborative data-sharing initiatives. By synthesizing existing literature and identifying research advancements, this paper aims to provide researchers and clinicians with insights into the current state and prospects of EEG-based BCI for Alzheimer’s disease, ultimately contributing to the development of more effective diagnostic and therapeutic interventions for this debilitating condition. Among getting brain data there is an attempt to exercise the prefrontal cortex to gain better control over memory and other parts of the brain.