Document Type
Article
Publication Date
7-2021
Abstract
Electroencephalogram (EEG) is a common tool used to understand brain activities. The data are typically obtained by placing electrodes at the surface of the scalp and recording the oscillations of currents passing through the electrodes. These oscillations can sometimes lead to various interpretations, depending on, for example, the subject’s health condition, the experiment carried out, the sensitivity of the tools used, or human manipulations. The data obtained over time can be considered a time series. There is evidence in the literature that epilepsy EEG data may be chaotic. Either way, the Embedding Theory in dynamical systems suggests that time series from a complex system could be used to reconstruct its phase space under proper conditions. In this letter, we propose an analysis of epilepsy EEG time series data based on a novel approach dubbed complex geometric structurization. Complex geometric structurization stems from the construction of strange attractors using Embedding Theory from dynamical systems. The complex geometric structures are themselves obtained using a geometry tool, the α-shapes from shape analysis. Initial analyses show a proof of concept in that these complex structures capture the expected changes brain in lobes under consideration. Further, a deeper analysis suggests that these complex structures can be used as biomarkers for seizure changes.
DOI
10.1162/neco_a_01398
Publisher
MIT Press
Repository Citation
Kwessi, E. A., & Edwards, L. J. (2021). Analysis of EEG data using complex geometric structurization. Neural Computation, 33(7), 1942-1969. http://doi.org/10.1162/neco_a_01398
Publication Information
Neural Computation
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.