A brain-computer interface (BCI) is a system enabling direct communication between neural activity in the brain and an external device, bypassing conventional neuromuscular pathways. BCIs translate electrical brain signals into computer commands, opening new possibilities in neurotechnology, neuroscience, medicine, and human-machine interaction. This concept differs from other interface technologies, such as traditional human-computer interfaces, by establishing a direct connection to neural activity without muscular or peripheral mediation.
Use cases and examples
BCIs are used in medicine to restore motor functions for patients with paralysis or neurodegenerative diseases (e.g., controlling robotic prostheses or computer cursors). They are also explored for assisted communication in locked-in syndrome patients, neurorehabilitation, and exoskeleton control. Outside of medicine, BCIs appear in video games, virtual reality, immersive environments, and for controlling connected devices.
Main software tools, libraries, frameworks
Several open source and commercial tools and frameworks exist for BCI development, such as OpenBCI, BCI2000, OpenViBE, and LabStreamingLayer. For EEG signal analysis, libraries like MNE-Python, EEGLAB (MATLAB), or FieldTrip are widely used. Proprietary solutions like g.tec, Emotiv, or NeuroPype offer integrated platforms for research or clinical applications.
Latest developments, evolutions, and trends
Recent advances focus on improving spatial and temporal resolution of recorded signals, integrating artificial intelligence to better interpret user intent, and enhancing miniaturization and portability. Trends include developing more effective non-invasive BCIs, expanding long-term implantable solutions (e.g., Neuralink), and exploring applications in cognitive augmentation and multi-user interaction. Key challenges remain in ethics, data security, and societal acceptance.