Yulia Bezsudnova

Papers

Total Citations

103

H-Index

About

Yulia Bezsudnova is a leading researcher in the field of assistive robotics and brain-computer interfaces (BCIs), with a particular focus on non-invasive systems for human–machine interaction. Her most-cited work, "Shared control of a robotic arm using non-invasive brain–computer interface and computer vision guidance" (2019, 103 citations), represents a pivotal contribution to the development of practical, real-world BCI applications. In this study, she demonstrated how integrating computer vision with electroencephalography (EEG)-based control can significantly enhance the precision and usability of robotic arms for individuals with motor impairments, reducing user fatigue while maintaining high levels of autonomy. This work bridges the gap between neural decoding and autonomous robotic assistance, offering a scalable framework for shared control that has influenced subsequent research in rehabilitation robotics. Bezsudnova’s research is distinguished by its emphasis on user-centered design and robust, non-invasive technologies, making her a key figure in advancing accessible assistive devices. Her contributions not only push the boundaries of BCI robustness but also underscore the potential for integrating AI-driven guidance with neural signals to restore motor function, impacting both clinical and engineering communities.