Covert visuospatial attention Brain-Computer Interface for control and rehabilitation

At the current stage, Brain-Computer Interfaces (BCIs) represent a promising technology for communication and control of assistive devices as well as for the clinical motor rehabilitation after a stroke. Current BCI systems may be divided in two main typologies, either based on the detection of the neural correlates elicited by predefined external stimuli or on the recognition of the self paced brain patterns related to mental imagination tasks. Nevertheless, in literature different observations have shown that not everyone can reach a good level of control with any of the current BCI typologies. In this regard, this thesis aims to show the suitability of a novel control signal for BCI: Covert Visuospatial Attention (CVSA). CVSA represents the ability of anyone to focus his attention at one point in space without the need of overt eye movements. This modality brings several advantages: first of all, it relies on the actual execution of an action (i.e., focusing attention) rather than on the pure task imagination. This represents an important benefit since it defines a control signal for BCI that it is natural, spontaneous and as close as possible to the daily experience of the user. Furthermore, it is totally gaze-independent and thus, it can be used by people with no (or limited) gaze control. Finally, CVSA does not require any external stimulation but can be exploited by a voluntary modulation of brain patterns. Recently, different studies started investigating CVSA to identify its neural correlates in scalp EEG. Nevertheless, no attempts of using CVSA for online EEG BCI operations are reported in literature. This thesis aims to demonstrate that an online EEG BCI based on CVSA is actually feasible and suitable. Therefore, three key aspects have been taken in account: (i) a new methodology for the detection and classification of CVSA from EEG signals, (ii) the evaluation of the online BCI operations by healthy users and, finally, (iii) the identification and the testing of two possible applications of CVSA BCI oriented to disabled users. The first part of this thesis is devoted to the definition of a new method for single trial classification of CVSA: the time-dependent approach. Previous studies have already demonstrated the involvement of the α-power over the parieto-occipital regions of the brain during CVSA tasks. The new method proposed here, extends these findings by means of more detailed analysis on the α sub-bands and on the temporal structure of the brain patterns. The intuition behind is twofold: on one hand, only specific bands are actually carrying discriminative information during the spatial attention tasks. On the other hand, attention-related patterns are evolving over time and consequentially a time-dependent approach would enhance the BCI performance. The analysis performed fully confirmed both the hypothesis and showed that the time-dependent approach can increase the classification accuracy of 12.3% (on average) with respect