Joint BSS as a natural analysis framework for EEG-hyperscanning

Jonas Chatel-Goldman (1) , Marco Congedo (1) , Ronald Phlypo (2)

1 GIPSA-VIBS - GIPSA - Vision and Brain Signal Processing
2 MLSP - Machine Learning for Signal Processing Laboratory

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.

Mots clés: Brain Coupling; dual-EEG; SSVEP; joint BSS; Hyperscanning

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Jonas Chatel-Goldman, Marco Congedo, Ronald Phlypo. Joint BSS as a natural analysis framework for EEG-hyperscanning. ICASSP 2013 - 38th IEEE International Conference on Acoustics, Speech and Signal Processing, May 2013, Vancouver, Canada. pp.1212-1216. ⟨hal-00831690⟩