Fusing minds and machines: A next-generation review of information-driven brain-computer interface technologies for precision healthcare

Brain–Computer Interfaces (BCIs) represent a rapidly evolving neurotechnology that enables direct communication and control between the brain and external devices, bypassing conventional neuromuscular pathways. This review presents a structured and critical technical overview of the BCI field, tracing its foundations in basic neurophysiology to contemporary advances in neuroprostheses and neurostimulation. A comprehensive survey of signal acquisition modalities, including non-invasive (Electroencephalography (EEG), Magnetoencephalography (MEG), Functional Near-Infrared Spectroscopy (fNIRS)), semi-invasive (Electrocorticography (ECoG)), and invasive systems (intracortical microelectrode arrays), is provided, with clear comparison of their operating principles, spatiotemporal resolution, signal fidelity, and clinical translational potential. The article systematically breaks down the conventional BCI signal processing approach, detailing preprocessing, artifact handling, advanced feature extraction (e.g., Common Spatial Patterns (CSP)), and the use of machine learning techniques (involving linear classifiers, Support Vector Machines (SVMs), and modern deep learning architectures). The review further compares principal BCI interaction paradigms—including Motor Imagery (MI), P300 Event-Related Potentials (ERP), and Steady-State Visually Evoked Potentials (SSVEP)—evaluating them with respect to information transfer rate (ITR), user training requirements, robustness, and practical applicability. The review also traces how BCI applications have grown over time, from enabling communication for people with locked-in syndrome to supporting movement recovery in stroke patients. Modern BCIs can now control systems with several degrees of freedom, providing better control and accuracy. The discussion also addresses major challenges in this area, such as signal instability, long-term hardware effects, and important ethical, legal, and social issues. It wraps up by discussing the future frontiers of BCI research, such as hybrid systems, closed-loop neurostimulation, brain-to-brain communication, and the increasingly deep symbiosis with advanced artificial intelligence, thereby serving as a comprehensive and structured technical overview for researchers and practitioners in neuroscience, biomedical engineering, and related interdisciplinary domains.