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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.1//EN"
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<title>Re-thinking EEG-based non-invasive brain interfaces: modeling and analysis</title>
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<h1>Re-thinking EEG-based non-invasive brain interfaces: modeling and analysis</h1>
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<table class="imgtable"><tr><td>
<img src="pics/bciProcess.png" alt="bci" width="800px" height="300px" /> </td>
<td align="left"></td></tr></table>
<table class="imgtable"><tr><td>
<img src="pics/brainEEG.png" alt="bci" width="650px" height="300px" /> </td>
<td align="left"></td></tr></table>
<p><b>Abstract:</b> 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.<br />
<a href="https://dl.acm.org/citation.cfm?id=3207896.3207928">[paper</a>]</p>
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