Joint EEG-fMRI model for EEG source separation

Electroencephalography (EEG) offers a rich representation of human brain activity in the time domain. EEG would in many circumstances be the preferred technique for analysing brain activity, as it is less expensive and more practical to use than other modalities like functional Magnetic Resonance Imaging (fMRI), notably due to its size. However, its spatial resolution is limited, hampering its ability to characterise activity across spatially distributed brain networks. In comparison, functional Magnetic Resonance Imaging (fMRI) offers very good spatial resolution but the haemodynamic nature of the signal limits its temporal resolution to the order of seconds. A possible solution to this problem is to use both EEG and fMRI signals, but this approach would lead to the loss of convenience of EEG alone. We would like to bring in the advantages of fMRI signal into EEG assessment of brain state and brain responses without the necessity for the presence of the fMRI equipment on site. In this article, we propose a joint statistical model of fMRI/EEG signals and then exploit the learnt correlations to improve the results of signal processing of EEG on its own. We compare the performance of a Blind Source Separation (BSS) method on its own with one, which uses our joint EEG-fMRI model, and show the improvement in the precision of the source separation.