Mental workload evaluation using weighted phase lag index and coherence features extracted from EEG data.

ABSTRACT: Electroencephalogram (EEG) represents an effective, non-invasive technology to study mental workload. However, volume conduction, a common EEG artifact, influences functional connectivity analysis of EEG data. EEG coherence has been used traditionally to investigate functional connectivity between brain areas associated with mental workload, while weighted Phase Lag Index (wPLI) is a measure that improves on coherence by reducing susceptibility to volume conduction, a common EEG artifact. The goal of this study was to compare two methods of functional connectivity analysis, wPLI and coherence, in the context of mental workload evaluation. The study involved model development for mental workload domains and comparing their performance using coherence-based features, wPLI-based features, and

SUBMITTER: Shafiei SB 

PROVIDER: S-EPMC11734752 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature