Braincomputer

A brain-computer interface (BCI) is a system that creates a direct pathway for communication between the brain and an external device, such as a computer, prosthetic limb, or wheelchair. It translates neural activity into actionable commands without requiring muscle movement. In scholarly writing the standard term is brain-computer interface, while the combined form braincomputer is sometimes seen as shorthand.

BCIs use signals recorded from the brain through non-invasive methods like electroencephalography (EEG), magnetoencephalography (MEG), or functional near-infrared spectroscopy (fNIRS), or invasive methods with implanted electrodes such as an intracortical array. Signal processing and machine learning decode intent, translating patterns of brain activity into control signals. Output devices may include cursors, robotic arms, communication software, or neuromodulation systems.

History: Early experiments in the 1960s to 1980s demonstrated basic control of devices by brain signals, with more robust systems emerging in the 1990s and 2000s. Modern BCIs have enabled people with severe paralysis to type, spell, or move assistive devices.

Applications: medical (assistive communication for locked-in patients, neurorehabilitation after stroke), assistive technology, research, and potential consumer electronics. Some research aims include bidirectional BCIs that provide sensory feedback, and wireless, minimally invasive implants.

Challenges and ethics: safety and long-term stability of implants, signal variability, calibration, device longevity, data privacy, informed consent, and the risk of coercion or unfair access. Regulatory frameworks govern clinical use, safety testing, and data protection.

The field continues to evolve with advances in neuroscience, materials science, and AI, aiming to improve reliability, reduce invasiveness, and expand functional capabilities while addressing ethical implications.