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Brain interfaces are cyber-physical systems that aim to harvest informationfrom the (physical) brain through sensing mechanisms, extract information aboutthe underlying processes, and decide/actuate accordingly. Nonetheless, thebrain interfaces are still in their infancy, but reaching to their maturityquickly as several initiatives are released to push forward their development(e.g., NeuraLink by Elon Musk and `typing-by-brain' by Facebook). This hasmotivated us to revisit the design of EEG-based non-invasive brain interfaces.Specifically, current methodologies entail a highly skilled neuro-functionalapproach and evidence-based \emph{a priori} knowledge about specific signalfeatures and their interpretation from a neuro-physiological point of view.Hereafter, we propose to demystify such approaches, as we propose to leveragenew time-varying complex network models that equip us with a fractal dynamicalcharacterization of the underlying processes. Subsequently, the parameters ofthe proposed complex network models can be explained from a system'sperspective, and, consecutively, used for classification using machine learningalgorithms and/or actuation laws determined using control system's theory.Besides, the proposed system identification methods and techniques havecomputational complexities comparable with those currently used in EEG-basedbrain interfaces, which enable comparable online performances. Furthermore, weforesee that the proposed models and approaches are also valid using otherinvasive and non-invasive technologies. Finally, we illustrate andexperimentally evaluate this approach on real EEG-datasets to assess andvalidate the proposed methodology. The classification accuracies are high evenon having less number of training samples.

Re-thinking EEG-based non-invasive brain interfaces: modeling and analysis