1. A method of estimating the likelihood of brain concussion from neurophysiological data acquired from the brain of the subject, comprising: by a set of electrodes placed on a scalp of a subject, acquiring the neurophysiological data from the brain of the subject; and using a data processor for: identifying activity-related features in said data wherein each feature is a vector of data characteristics; clustering said vectors to provide a plurality of clusters; calculating for at least one pair of clusters, at least one cluster property selected from the group consisting of (i) a number of vectors in a corresponding pair of clusters; (ii) a variability among numbers of vectors in said corresponding pair of clusters; (iii) a width of time windows associated with each cluster of said corresponding pair of clusters; (iv) a latency difference separating said corresponding pair of clusters; constructing a subject-specific brain network activity (BNA) pattern having a plurality of nodes, wherein each node represents a cluster of said plurality of clusters, and wherein each pair of nodes is assigned with a connectivity weight which comprises a weight index calculated based on said calculated properties; storing said constructed BNA pattern in a memory; retrieving said constructed BNA pattern and a baseline BNA pattern being specific to the subject from said memory; calculating a BNA pattern similarity describing a comparison between said constructed BNA pattern and said baseline BNA pattern; assessing the likelihood of brain concussion responsively to said BNA pattern similarity; and generating on a display an output indicative of said likelihood.