Brain-Computer Interfaces (BCIs) that use electroencephalography (EEG) are essential for facilitating direct brain-to-external device communication, especially for people with severe movement impairments. This paper offers a thorough analysis of the most recent methods for EEG signal analysis used to improve BCI performance. We explore sophisticated techniques for feature extraction, classification, and signal preprocessing, emphasizing their contributions to enhancing the precision and effectiveness of BCIs. We also investigate applications in a variety of fields, including emotion identification, motor control, and cognitive state monitoring. This study attempts to direct future research and development in EEG-based BCIs by combining insights from more than 20 influential works in the field. Our results highlight how crucial it is to combine machine learning methods with reliable signal processing techniques in order to enhance the capabilities of neuro-technological systems.