Simplified EEG inverse solution for BCI real-time implementation

EEG brain imaging has become a promising approach in Brain-computer interface applications. However, accurate reconstruction of active regions and computational burden are still open issues. In this paper, we propose to use a simplified forward model that includes the reduction of the cortical dipoles based on Brodmann areas together with state-of-the-art EEG brain imaging techniques. With this approach the well known Beamformers and Greedy Search inverse solutions become feasible for real-time implementation, while guaranteeing lower localization error than previous approaches used in BCI. This methodology was tested with synthetic and real EEG data from a visual attention study. Results show zero localization error in terms of active cortical regions estimation in single 1 s trial datasets, with a computation time of 1.1 s in a non-specialized personal computer. These results open the possibility to obtain in real-time information of active cortical regions in Brain-computer interfaces.