Brain Computer Interfaces (BCI) is an area of research that is rapidly growing in the neuroscience and bioengineering fields. One popular approach to the generation of a BCI system consist in the recognition by a computer of the patterns of electrical activity on the scalp gathered from a series of electrodes. One of the problems related to the use of surface EEG is the blurring effect due to the smearing of the skull on the transmission of the potential distribution from the cerebral cortex toward the scalp electrodes. This happens since the skull has a very low electric conductivity when compared with the scalp or the brain ones. The blurring effect makes the EEG data gathered from the scalp electrodes rather correlated, a problem not observed in the cortical EEG data recorded from the invasive implants in monkeys and man. Such correlation makes problematic the work of the classifiers, since the features extracted from the different scalp electrodes tends to be rather similar and this correlation is hard to be disentangled with blind methods like Principal Component Analysis. In the last decade, high-resolution EEG technologies have been developed to enhance the spatial information content of EEG activity. Furthermore, since the ultimate goal of any EEG recording is to provide useful information about the brain activity, a body of mathematical techniques, known as inverse procedures, has been developed to estimate the cortical activity from the raw EEG recordings. Examples of these inverse procedures are the dipole localization, the distributed source and the cortical imaging techniques. Inverse procedures could use linear and non linear techniques to localize putative cortical sources from EEG data, by using mathematical models of the head as volume conductor. More recently, it has been suggested that with the use of the modern high resolution EEG technologies it could be possible to estimate the cortical activity associated to the mental imagery of the upper limbs movements in humans better than with the scalp electrodes. In this presentation we will review main achievements in the field of the Brain Computer Interfaces and we will demonstrate how it is possible run a BCI system able to drive and control several electronic and robotic devices in a house environment. In particular, we first describe a BCI system used on a group of normal subjects in which the technology of the estimation of the cortical activity is illustrated. Then, we used the BCI system for the command of several electronic devices within a three-room environment employed for the neurorehabilitation.
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