Brainmachine

Brainmachine, or brain-machine interface (BMI), refers to systems that establish a direct communication pathway between the brain and an external device. These systems can read neural activity to control devices and, in some designs, provide sensory feedback to the brain. Brain-machine interfaces are typically categorized as invasive or non-invasive depending on whether the neural signals are recorded from implanted hardware.

A BMI usually comprises a neural interface (electrodes or sensors), a signal processing and decoding pipeline, and an actuator or device controlled by the decoded commands. In invasive systems, electrode arrays or electrocorticography grids record action potentials or local field potentials from cortex; non-invasive approaches rely on EEG, MEG, or near-infrared spectroscopy.

The concept emerged from neurophysiology experiments in the late 20th century and achieved landmark demonstrations in the 1990s and 2000s. Invasive human trials led to high-profile results, such as a paralyzed patient using motor cortex signals to control a computer cursor and selecting targets on a screen.

Applications include helping people with motor impairments communicate and interact with the world, control prosthetic limbs, or aid rehabilitation. Research also explores bidirectional feedback, offering sensations such as tactile feedback to improve control.

Challenges include biocompatibility, signal stability over time, surgical risks, power and data transfer in implants, and ethical issues around privacy, autonomy, and enhancement. Regulation and safety standards guide clinical development.

Looking ahead, developments aim at wireless, less invasive interfaces with improved decoders and real-time closed-loop control, broader clinical adoption, and potential uses in augmented reality, gaming, and human augmentation, while addressing ethical and societal implications.