Ricerc@Sapienza

The detection of mental states is fundamental for many applications as brain-computer interface (BCI). Currently, in the majority of works, features related to the activity of single brain regions is used, as power spectral density. Performances of BCI are non-optimal and a percentage of 20-30% of users is not able to use the interface. For these reasons, there is an increasing interest in developing new metrics and algorithms, focusing, in the majority of cases, in the classification block. On the contrary, the aim of the project is introducing a new feature for the control of brain computer interface. The proposed feature is the connectivity, that describes the functional interaction between different brain areas and it can be descriptive of complex tasks as motor imagery.
To the end of integrating connectivity metrics in BCI, the features extraction has to be online and it is necessary to develop a time-varying estimate of connectivity. For this reason, this project focuses in developing a method for the estimation of connectivity neglecting the hypothesis of stationarity of the brain signals, which are strongly non-stationary because of the rapid changing of user¿s brain activity to to control the BCI.