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The extraction of brain functioning features is a crucial step in thedefinition of brain-computer interfaces (BCIs). In the last decade, functionalconnectivity (FC) estimators have been increasingly explored based on theirability to capture synchronization between multivariate brain signals. However,the underlying neurophysiological mechanisms and the extent to which they canimprove performance in BCI-related tasks, is still poorly understood. Toaddress this gap in knowledge, we considered a group of 20 healthy subjectsduring an EEG-based hand motor imagery (MI) task. We studied twowell-established FC estimators, i.e. spectral- and imaginary-coherence, andinvestigated how they were modulated by the MI task. We characterized theresulting FC networks by extracting the strength of connectivity of each EEGsensor and compared the discriminant power with respect to standard powerspectrum features. At the group level, results showed that whilespectral-coherence based network features were increasing the controlateralmotor area, those based on imaginary-coherence were decreasing. We demonstratedthat this opposite, but complementary, behavior was respectively determined bythe increase in amplitude and phase synchronization between the brain signals.At the individual level, we proved that including these network connectivityfeatures in the classification of MI mental states led to an overallimprovement in accuracy. Taken together, our results provide fresh insightsinto the oscillatory mechanisms subserving brain network changes during MI andoffer new perspectives to improve BCI performance.

Phase/amplitude synchronization of brain signals during motor imagery BCI tasks