Joint BSS as a natural analysis framework for EEG-hyperscanning

Recent advances in Joint Blind Source Separation (JBSS) extend the BSS framework to the simultaneous source separation of multiple datasets. In this paper we provide a comparative study of four such JBSS algorithms on human dual-electroencephalographic (dual-EEG) data. Appropriateness of second order JBSS is demonstrated for concurrent estimation of correlated sources in a multi-subject synchronous steady-state visually evoked potentials experiment. This approach gives a new starting point for the exploration of brain activities in a hyperscanning framework.