Ricerc@Sapienza

A Brain Computer Interface is defined as a system that measures and analyze brain signals and converts them in real-time into outputs that do not depend on the normal output pathways of peripheral nerves and muscles. Nowadays, a BCI system is used in two different ways: 1) as tool for enabling communication with external environment or controlling some external devices; 2) as rehabilitation tool for stimulating brain plasticity in all those patients who shown motor deficit after stroke. In the latter case, BCI interfaces are controlled through the amplitude of a well-known cerebral rhythm, the sensory motor rhythm, which is evident over the sensorimotor areas in 8-13 Hz frequency band. Since previous findings in the field described plasticity phenomena at the basis of the motor recovery as modifies in brain networks, it could be interesting to develop BCI systems using as controlling features, measures derived by such brain networks. The estimation of brain networks from scalp EEG signals represents a help in moving a step forward in this field. However, it is difficult to estimate brain connectivity in an on-line setting (BCI use) due to the scarce amount of data samples available for the estimate. What is still missing is: i) a methodological solution in estimating brain connectivity ideally in on-line environment; ii) a toolbox for brain connectivity estimation to be integrated in a BCI system. The main objective of the proposed project consists in the development and implementation of an accurate and reliable toolbox for brain connectivity estimation to be used in a BCI system. Such toolbox will be the result of an integration between advanced methodologies for brain connectivity analysis and Machine Learning. The developed toolbox will be integrated in a BCI system that will be tested in a group of 10 healthy subjects performing the motor imagery task. The project will provide a new BCI system able to train brain plasticity after a damage in motor functions.