Siamac Fazli
Papers
2
Total Citations
269
H-Index
2
About
Siamac Fazli’s research sits at the intersection of brain-computer interfaces (BCIs), machine learning, and assistive robotics, with a focus on restoring motor function for individuals with severe paralysis. His most influential work, a 2012 clinical study with 174 citations, demonstrated the feasibility of using motor imagery EEG-based BCIs to control assistive robotic arms in chronic tetraplegics, providing long-term follow-up evidence that such systems can be practical for daily use. This study remains a cornerstone for translating BCI technology from lab to real-world application. Fazli has also advanced the field of multimodal neuroimaging, developing multivariate machine learning methods that fuse data from different imaging modalities—work that has garnered 95 citations and broad relevance across engineering and neuroscience. His contributions are notable for bridging rigorous computational analysis with patient-centered clinical trials, ensuring his research directly addresses the needs of individuals with spinal cord injuries. Through these efforts, Fazli has helped shape how BCIs can be integrated into assistive technologies, offering tangible hope for restoring independence.
Research Focus
Key Achievements
Top Papers
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