Andrew Henderson

Papers

Total Citations

H-Index

About

Andrew Henderson’s research bridges the frontiers of brain-computer interfaces (BCIs) and intelligent manufacturing systems. His early, pioneering work demonstrated how non-invasive EEG-based BCIs could enable individuals with severe neuromuscular disorders to control industrial robots, such as the Staubli TX40, using only brain waves. This foundational study, cited 18 times, opened new pathways for assistive robotics and human-machine collaboration. More recently, Henderson has advanced predictive maintenance through his work on vibration analysis utilizing unsupervised learning. His 2019 paper, with 12 citations, tackles a critical industrial challenge: transforming raw sensor data into actionable maintenance schedules without costly labeled datasets. By applying machine learning to vibration, temperature, and acoustic signals, he enables manufacturers to move from reactive repairs to proactive, data-driven health monitoring. Henderson’s contributions are notable for their dual impact—advancing both human-centric assistive technology and industrial efficiency. His research exemplifies how computational methods can solve real-world problems, from restoring communication for disabled individuals to optimizing factory operations. For students and researchers, Henderson’s work offers a compelling model of interdisciplinary innovation at the intersection of neuroscience, robotics, and data science.