Joint EEG-fMRI signal model for EEG separation and localization
Peng Sun · ORCA Online Research @Cardiff (Cardiff University) · 2015
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 such as functional Magnetic Resonance Imaging (fMRI), notably due to its size. However, its spatial resolution is limited, which hampers its ability to characterise activity across spatially distributed brain networks. In comparison, functional Magnetic Resonance Imaging (fMRI) offers very good spatial resolution but the hemodynamic 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. Hence it is desirable to bring the advantages of an fMRI signal into EEG assessment of the brain’s state and responses without the necessity for the presence of fMRI equipment on site. In this work, a joint statistical model of fMRI/EEG signals is proposed and used for processing of EEG signals. The performance of a standard Blind Source Separation (BSS) method is compared with the new method, which uses the above joint EEG-fMRI model, which in turn shows improvement in the precision of both source separation and localisation.