Re-thinking EEG-based non-invasive brain interfaces: modeling and
analysis
release_r7osyi5ktngnzhudvjgzlsbsfe
by
Gaurav Gupta, Sergio Pequito, Paul Bogdan
2018
Abstract
Brain interfaces are cyber-physical systems that aim to harvest information
from the (physical) brain through sensing mechanisms, extract information about
the underlying processes, and decide/actuate accordingly. Nonetheless, the
brain interfaces are still in their infancy, but reaching to their maturity
quickly as several initiatives are released to push forward their development
(e.g., NeuraLink by Elon Musk and `typing-by-brain' by Facebook). This has
motivated us to revisit the design of EEG-based non-invasive brain interfaces.
Specifically, current methodologies entail a highly skilled neuro-functional
approach and evidence-based a priori knowledge about specific signal
features and their interpretation from a neuro-physiological point of view.
Hereafter, we propose to demystify such approaches, as we propose to leverage
new time-varying complex network models that equip us with a fractal dynamical
characterization of the underlying processes. Subsequently, the parameters of
the proposed complex network models can be explained from a system's
perspective, and, consecutively, used for classification using machine learning
algorithms and/or actuation laws determined using control system's theory.
Besides, the proposed system identification methods and techniques have
computational complexities comparable with those currently used in EEG-based
brain interfaces, which enable comparable online performances. Furthermore, we
foresee that the proposed models and approaches are also valid using other
invasive and non-invasive technologies. Finally, we illustrate and
experimentally evaluate this approach on real EEG-datasets to assess and
validate the proposed methodology. The classification accuracies are high even
on having less number of training samples.
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