Topological EEG-Based Functional Connectivity Analysis for Mental Workload State Recognition

Mental workload (MWL) assessment is crucial in fatigue evaluation applications to avoid potential health problems or serious accidents. This article proposes an MWL recognition approach developed with a topological investigation of the electroencephalography (EEG)-based brain functional connectivity (FC) network. In this work, the graph-filtration-based features are extracted to reveal the brain state variations using the persistent homology technique from the topological data analysis (TDA) area. Three public open benchmark datasets are used to test and verify the recognition ability of the proposed method, which are developed with the MWL assessment experiments of Simultaneous Capacity (SIMKAP) test tasks, arithmetic calculations, and Multi-Attribute Task Battery II (MATB-II) MWL tasks. The experimental results show that the proposed topological FC network analysis scheme shows excellent distinguishing ability in brain state recognition, comparable to or better than the state-of-art results with similar settings. This work is the first investigation of EEG-based MWL evaluation with the persistent homology analysis of multivariate time series. The proposed topological features are effective and robust brain states’ indicators, providing an alternative feature in designing novel brain–computer interface systems.