Functional Semi-Blind Source Separation Identifies Primary Motor Area Without Active Motor Execution

High time resolution techniques are crucial for investigating the brain in action. Here, we propose a method to identify a section of the upper-limb motor area representation (FS-M1) by means of electroencephalographic (EEG) signals recorded during a completely passive condition (FS-M1bySS). We delivered a galvanic stimulation to the median nerve and we applied to EEG the semi-Blind Source Separation (s-BSS) algorithm named Functional Source Separation (FSS). In order to prove that FS-M1bySS is part of FS-M1, we also collected EEG in a motor condition, i.e. during a voluntary movement task (isometric handgrip) and in a rest condition, i.e. at rest with eyes open and closed. In motor condition, we show that the cortico-muscular coherence (CMC) of FS-M1bySS does not differ from FS- M1 CMC (0.04 for both sources). Moreover, we show that the FS-M1bySS's ongoing whole band activity during Motor and both rest conditions displays high mutual information and time correlation with FS-M1 (above 0.900 and 0.800, respectively) whereas much smaller ones with the primary somatosensory cortex S1 (about 0.300 and 0.500, p < 0.001). FS-M1bySS as a marker of the upper-limb FS-M1 representation obtainable without the execution of an active motor task is a great achievement of the FSS algorithm, relevant in most experimental, neurological and psychiatric protocols.