Daniel Prince

Papers

Total Citations

H-Index

About

Daniel Prince is a pioneering researcher in brain-computer interfaces (BCIs) and assistive robotics, best known for his foundational work on non-invasive EEG-based control systems. His most cited paper, "Brain machine interface using Emotiv EPOC to control robai cyton robotic arm" (2015, 16 citations), established a critical framework for translating raw electroencephalography data into actionable commands for robotic manipulation. Prince developed and validated a complete software suite that recognizes human thought patterns and pairs them with precise motor actions, enabling direct neural control of a robotic arm without invasive surgery. This work demonstrated that consumer-grade EEG headsets like the Emotiv EPOC could achieve reliable real-time control, significantly lowering the barrier to entry for BCI research. Though his citation count reflects the niche but growing nature of his field, Prince's contribution is notable for its practical, open-architecture approach—providing a reproducible blueprint that has enabled subsequent researchers to build upon accessible, low-cost neural interfaces. His research sits at the intersection of signal processing, machine learning, and human-robot interaction, with direct implications for assistive technologies serving individuals with motor disabilities.