The article provides a comprehensive review of brain-computer interfaces (BCIs), focusing on the technological advancements and applications. BCIs are systems that enable communication and control of external devices using brain signals, primarily for individuals with severe motor disabilities. The review covers the five key stages of BCI operation: signal acquisition, preprocessing, feature extraction, classification, and control interface. It discusses various neuroimaging modalities used in signal acquisition, including electroencephalography (EEG), magnetoencephalography (MEG), electrocorticography (ECoG), intracortical neuron recording, functional magnetic resonance imaging (fMRI), and near-infrared spectroscopy (NIRS). Each modality is evaluated based on its advantages, limitations, and suitability for different applications. The article also explores different 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). These signals are analyzed in terms of their physiological basis, detection methods, and potential for effective BCI control. The review highlights the challenges and recent advancements in each area, emphasizing the importance of signal processing techniques to enhance the performance of BCIs. Finally, the article surveys various BCI applications, including communication devices, prosthetic control, and rehabilitation tools. It concludes by discussing the future directions and challenges in BCI research, emphasizing the need for standardized frameworks and further integration of multidisciplinary expertise.The article provides a comprehensive review of brain-computer interfaces (BCIs), focusing on the technological advancements and applications. BCIs are systems that enable communication and control of external devices using brain signals, primarily for individuals with severe motor disabilities. The review covers the five key stages of BCI operation: signal acquisition, preprocessing, feature extraction, classification, and control interface. It discusses various neuroimaging modalities used in signal acquisition, including electroencephalography (EEG), magnetoencephalography (MEG), electrocorticography (ECoG), intracortical neuron recording, functional magnetic resonance imaging (fMRI), and near-infrared spectroscopy (NIRS). Each modality is evaluated based on its advantages, limitations, and suitability for different applications. The article also explores different 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). These signals are analyzed in terms of their physiological basis, detection methods, and potential for effective BCI control. The review highlights the challenges and recent advancements in each area, emphasizing the importance of signal processing techniques to enhance the performance of BCIs. Finally, the article surveys various BCI applications, including communication devices, prosthetic control, and rehabilitation tools. It concludes by discussing the future directions and challenges in BCI research, emphasizing the need for standardized frameworks and further integration of multidisciplinary expertise.