Subject-Specific Cognitive Workload Classification Using EEG-Based Functional Connectivity and Deep Learning

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 20; p. 6710
• Main Authors: Gupta, Anmol, Siddhad, Gourav, Pandey, Vishal, Roy, Partha Pratim, Kim, Byung-Gyu
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 09.10.2021; MDPI
• Subjects: Accuracy; Brain research; Classification; CNN; cognitive workload; Collaboration; Deep learning; Electroencephalography; Emotional intelligence; functional connectivity analysis; LSTM; Machine learning; Memory; mental workload; mutual information; Physiology; Questionnaires; Topography; Workloads

Cognitive workload is a crucial factor in tasks involving dynamic decision-making and other real-time and high-risk situations. Neuroimaging techniques have long been used for estimating cognitive workload. Given the portability, cost-effectiveness and high time-resolution of EEG as compared to fMRI...