Topological Signal Processing in Neuroimaging Studies

Abstract

Electroencephalography (EEG) is an important neuroimaging tool for understanding network disorders caused by neuroanatomical malformation or damage such as epilepsy and post-stroke aphasia. Topological data analysis (TDA) can decode patterns in EEG signals that are not captured by standard temporal and spectral features but at the same time reveal important information on the underlying brain processes of clinical interest. The heterogeneity of conditions associated with brain network disorders renders it highly challenging to develop statistical methods for analyzing topological features in patients’ EEG signals. In this paper, we advance a generalized topological signal processing framework for extracting and analyzing topological features in EEG signals. The framework is applied to study EEG correlates of neural deficits in post-stroke aphasia patients.