This review provides an overview of brain-computer interfaces (BCIs), discussing their components, technologies, and applications. BCIs are systems that allow individuals with severe motor disabilities to communicate or control devices using brain signals. The review outlines the key stages in BCI development: signal acquisition, preprocessing, feature extraction, classification, and control interface. It discusses various neuroimaging techniques used for signal acquisition, including electroencephalography (EEG), magnetoencephalography (MEG), and electrocorticography (ECoG), as well as non-invasive and invasive methods. The review also covers different types of control signals used in BCIs, such as visual evoked potentials (VEPs), slow cortical potentials (SCPs), P300 evoked potentials, and sensorimotor rhythms (mu and beta rhythms). It highlights the advantages and challenges of each technology, including issues related to signal quality, noise, and the need for accurate classification. The review also discusses the applications of BCIs in rehabilitation, assistive technology, and communication for individuals with neurological disorders. Finally, it addresses the current state of BCI research, emphasizing the need for standardized frameworks and further advancements in signal processing and control methods to improve the effectiveness and usability of BCIs.This review provides an overview of brain-computer interfaces (BCIs), discussing their components, technologies, and applications. BCIs are systems that allow individuals with severe motor disabilities to communicate or control devices using brain signals. The review outlines the key stages in BCI development: signal acquisition, preprocessing, feature extraction, classification, and control interface. It discusses various neuroimaging techniques used for signal acquisition, including electroencephalography (EEG), magnetoencephalography (MEG), and electrocorticography (ECoG), as well as non-invasive and invasive methods. The review also covers different types of control signals used in BCIs, such as visual evoked potentials (VEPs), slow cortical potentials (SCPs), P300 evoked potentials, and sensorimotor rhythms (mu and beta rhythms). It highlights the advantages and challenges of each technology, including issues related to signal quality, noise, and the need for accurate classification. The review also discusses the applications of BCIs in rehabilitation, assistive technology, and communication for individuals with neurological disorders. Finally, it addresses the current state of BCI research, emphasizing the need for standardized frameworks and further advancements in signal processing and control methods to improve the effectiveness and usability of BCIs.