CNBI lab carries out research on the direct use of human brain signals to control devices and interact with our
environment. In this multidisciplinary research, we are bringing together our pioneering work on the two fields of brain-machine interfaces and adaptive intelligent robotics.
A brain-machine interface (BMI) monitors the user’s brain activity, extracts specific features from the brain signals that reflect the intent of the subject, and translates these features into actions —such as moving a wheelchair or
selecting a letter from a virtual keyboard without using activity of any muscle or peripheral nerve.

The central tenet of a BMI is the capability to distinguish different patterns of brain activity, each being associated
to a particular intention or mental task. Hence mutual adaptation is a key component, as (i) the brain-controlled device must learn to respond to the individual mental commands of the user and (ii) users must learn to modulate their
brainwaves voluntary to facilitate the recognition of their intent by the BMI.
Non-invasive BMIs mainly use electroencephalographic (EEG) activity recorded from electrodes placed on the scalp. It primarily measures the synchronous activity of thousands of cortical neurons.
EEG is a convenient, safe, non-invasive and inexpensive recording method that is ideal to bring BMI technology to a large population.

Indeed, the promise of BMI is to augment human capabilities by providing a new interaction link with the outside world
and is particularly relevant as an aid for paralyzed humans, although it also opens up new possibilities for able-bodied people —for instance, in space applications.