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 …

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

Traditional threshold-based methods in brain functional network analysis have some
drawbacks. First, the process of determining thresholds is often based on trial and error …

In this paper, we develop topological data analysis methods for classification tasks on
univariate time series. As an application, we perform binary and ternary classification tasks …

In the past few years, light, affordable wearable inertial measurement units have been
providing to clinicians and researchers the possibility to quantitatively study motor …

Existing tools for bifurcation detection from signals of dynamical systems typically are either
limited to a special class of systems or they require carefully chosen input parameters and a …

Introduction The study of attention has been pivotal in advancing our comprehension of
cognition. The goal of this study is to investigate which EEG data representations or features …

In several fields, topology is well adapted for analyzing big data and much more potent than
conventional data analysis methods. Persistent Homology is an algebraic method used in …

Modern lean premixed combustors are operated in ultra-lean mode to conform to strict
emission norms. However, this causes the combustors to become prone to lean blowout …

Topological Data Analysis is a fast-growing and promising approach that recently gains
popularity in the data science field. It utilizes topological and geometric measurements to …