DeepAI AI Chat
Log In Sign Up

Analysis of EEG data using complex geometric structurization

by   Eddy Kwessi, et al.

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 the subject's health condition, the experiment carried out, the sensitivity of the tools used, human manipulations etc. 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 paper, we propose an analysis of epilepsy electroencephalogram 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, namely 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.


page 6

page 13

page 14

page 15

page 18

page 22

page 26


Geometric feature performance under downsampling for EEG classification tasks

We experimentally investigate a collection of feature engineering pipeli...

Computational Topology Techniques for Characterizing Time-Series Data

Topological data analysis (TDA), while abstract, allows a characterizati...

Reconstruction and prediction of random dynamical systems under borrowing of strength

We propose a Bayesian nonparametric model based on Markov Chain Monte Ca...

Visualizing High Dimensional Dynamical Processes

Manifold learning techniques for dynamical systems and time series have ...

A Time Series Approach to Parkinson's Disease Classification from EEG

Firstly, we present a novel representation for EEG data, a 7-variate ser...

Geodesic Properties of a Generalized Wasserstein Embedding for Time Series Analysis

Transport-based metrics and related embeddings (transforms) have recentl...