Papers
4
Total Citations
55
H-Index
4
About
Arshia Arif is a rising researcher at the intersection of cognitive neuroscience, human-robot collaboration, and Industry 5.0. Her work focuses on understanding and optimizing human performance in smart manufacturing environments, particularly where humans work alongside collaborative robots (cobots). Arif’s key contributions include developing multimodal frameworks that integrate neural signals (EEG), subjective reports, and behavioral measures to assess cognitive workload in real-time factory settings—a critical step toward safer, more efficient human-robot teams. She has also pioneered a novel logistic regression-based classification method for motor imagery EEG signals, advancing brain-computer interface (BCI) technology for assistive applications. Her most cited paper (25 citations) on multimodal cognitive workload assessment in smart factories has quickly become a reference point for researchers exploring neuroergonomics in industrial contexts. More recently, Arif has extended her work into psychological safety, using neuroimaging to monitor mental health during human-robot collaboration—a timely contribution as automation reshapes the workforce. With over 55 citations in just two years, Arif is establishing herself as a key voice in creating human-centered, neuroadaptive manufacturing systems.
Research Focus
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