Electroencephalography (EEG) is a widely used cerebral activity measuring device for both
clinical and everyday life applications. In addition to denoising and potential classification, a …

We provide a short introduction to the field of topological data analysis (TDA) and discuss its
possible relevance for the study of complex systems. TDA provides a set of tools to …

In this paper we develop an alternative topological data analysis (TDA) approach for
studying graph representations of time series of dynamical systems. Specifically, we show …

Functional brain networks are often constructed by quantifying correlations between time
series of activity of brain regions. Their topological structure includes nodes, edges …

Background The scope of this work is to build a Machine Learning model able to predict
patients risk to contract a multidrug resistant urinary tract infection (MDR UTI) after …

Developing sensitive and reliable methods to distinguish normal and abnormal brain states
is a key neuroscientific challenge. Topological Data Analysis, despite its relative novelty …

Persistent homology is a recently developed theory in the field of algebraic topology to study
shapes of datasets. It is an effective data analysis tool that is robust to noise and has been …

Emotional recognition through exploring the electroencephalography (EEG) characteristics
has been widely performed in recent studies. Nonlinear analysis and feature extraction …

This article explores the applications of Topological Data Analysis (TDA) in the finance field,
especially addressing the primordial problem of asset allocation. Firstly, we build a rationale …

Persistent cohomology is a powerful technique for discovering topological structure in data.
Strategies for its use in neuroscience are still undergoing development. We …