Annelies Raes

Papers

Total Citations

H-Index

About

Annelies Raes is a pioneering researcher at the intersection of cognitive neuroscience and industrial engineering, whose work is shaping the future of human-machine collaboration in Industry 4.0. Her research focuses on cognitive load detection, neuroergonomics, and adaptive automation, with a particular emphasis on using electroencephalography (EEG) and electrooculography (EOG) to monitor worker states in real time. Raes’s most cited study, “Danger, high voltage! Using EEG and EOG measurements for cognitive overload detection in a simulated industrial context” (2022, 29 citations), demonstrates how neural and ocular signals can reliably identify moments of cognitive overload during complex assembly tasks. Her earlier foundational work, “Identifying predictive EEG features for cognitive overload detection in assembly workers in Industry 4.0” (2019, 14 citations), established key neural biomarkers that distinguish between manageable and excessive mental workload. These contributions are critical for designing intelligent, adaptive systems that can dynamically adjust task demands to prevent human error and enhance safety in automated manufacturing environments. By bridging basic cognitive neuroscience with applied industrial challenges, Raes is helping to ensure that as factories become smarter, they remain human-centered—protecting worker well-being while maximizing productivity in the age of smart manufacturing.