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Abstract

The urge to further our understanding of multimodal neural data has recently become an important topic due to the ever increasing availability of simultaneously recorded data from different neural imaging modalities. In case where the electroencephalogram (EEG) is one of the measurement modalities, it is of interest to relate a nonlinear function of the raw EEG time-domain signal, namely the dynamics of EEG bandpower, to another modality such as the hemodynamic response, as measured with near-infrared spectroscopy (NIRS) or functional magnetic resonance imaging (fMRI). In this work we tackle exactly this problem by defining a novel algorithm that we denote multimodal source power correlation analysis (mSPoC). The validity of the mSPoC approach is demonstrated for real-world multimodal data, obtained from a Brain-Computer Interface experiment, where mSPoC's ability to recover common sources from multimodal measurements is contrasted against an existing state-of-art approach represented by canonical correlation analysis (CCA).