A Novel Three-dimensional Multilayer Electroencephalography Paradigm

Abstract

Contemporary electroencephalography systems operate on a two-dimensional single-layer paradigm where signals from multiple layers of neuronal populations under an electrode are aggregated and recorded by that single electrode, leading to noisy signals and a lack of insight into neurological processes and keeping brain-to-brain communication, practical brain-computer interfaces and a host of applications in domains ranging from medicine to computing out of reach. Here, we introduce a novel three-dimensional multilayer electroencephalography (3D Multilayer EEG) paradigm – unlike the contemporary single-layer or two-dimensional (2D Single-layer EEG) paradigm – that leverages a nature-inspired conceptual framework in which approximations to carefully selected features of the source of the bio-signals are harnessed for characterization and manipulation of the underlying biological system. Effected through the simultaneous capture of distinct signal streams from multiple layers of neurons, this novel multilayer EEG paradigm could lead to effective computer-mediated brain-to-brain communication systems, a clearer understanding of neurological processes both in normal functioning and in disease as well as several orders of magnitude improvements in the information transfer rate in brain-computer interface systems – making these systems practical – as well as enabling a broad range of novel applications in domains ranging from medicine to social interactions, human factors including workplace optimization, economics, generic computing and human-machine interactions. Recent work demonstrating the direct imaging of signals propagating through myelinated axons and direct evidence that scalp EEG recordings can detect subcortical electrophysiological activity confirms the correctness of the principles underpinning our framework. We demonstrate the effectiveness of our novel 3D Multilayer EEG paradigm by formulating the null and alternative hypotheses for simultaneous multilayer EEG signal capture and relying on the results of analysis of a set of carefully designed experimental measurements to falsify the null hypothesis and validate the alternative hypothesis.

A Comprehensive Artificial Intelligence-Driven Healthcare System

“…Further enhancement of the technologies presented here through extended research and development activities (including manufacturing the novel three-dimensional multilayer EEG system [45], [46] introduced by Ekpar potentially permitting orders of magnitude higher electrode densities and better performance than conventional EEG systems) will expand the capabilities of society, lead to rehabilitation of those suffering from neurological conditions and generally improve the standard of living.…”

“…Furthermore, brain-computer interfaces (BCIs) based on a variety of paradigms including the motor imagery paradigm have been developed using a wide range of AI platforms and architectures including convolutional neural networks (CNNs) as well as traditional techniques such as principal component analysis [26]- [44]. For BCIs based on electroencephalography (EEG) data, the use of novel three-dimensional multilayer EEG systems [45], [46] can provide improved performance.…”