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

TL;DR

This paper introduces a novel approach to EEG-based non-invasive brain interfaces using time-varying complex network models, enabling better interpretation, classification, and potential control, with real EEG data validation.

Contribution

It proposes a new fractal dynamical modeling framework for EEG signals that simplifies interpretation and improves classification, diverging from traditional neuro-physiological methods.

Findings

High classification accuracy with fewer training samples

Models are computationally efficient for real-time use

Applicable to other invasive and non-invasive brain technologies

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 \emph{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.