Brain–machine interfaces (BMIs), or brain–computer interfaces, are an exciting multidisciplinary field that has grown tremendously during the last decade. In a nutshell, BMI is about transforming thought into action and sensation into perception. In a BMI system, neural signals recorded from the brain are fed into a decoding algorithm that translates these signals into motor output. This includes controlling a computer cursor, steering a wheelchair, or driving a robotic arm. A closed control loop is typically established by providing the subject with visual feedback of the prosthetic device. BMIs have tremendous potential to greatly improve the quality of life of millions of people suffering from spinal cord injury, stroke, amyotrophic lateral sclerosis, and other severely disabling conditions.
An important aspect of a BMI is the capability to distinguish between different patterns of brain activity, each being associated to a particular intention or mental task.

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