Amin Hekmatmanesh
Papers
3
Total Citations
39
H-Index
3
About
Amin Hekmatmanesh is a researcher whose work sits at the intersection of neural engineering, signal processing, and human-machine interaction, with a particular focus on Brain-Computer Interface (BCI) systems and physiological signal analysis. His most influential contributions center on developing advanced algorithms for decoding electroencephalography (EEG) signals, enabling meaningful applications in rehabilitation robotics and assistive technologies. In his highly cited 2018 work, Hekmatmanesh combined Common Spatial Pattern (CSP) algorithms with kernel linear discriminant analysis and generalized radial basis functions to improve motor imagery detection for BCI applications — a methodology with direct implications for prosthetics and human-controlled systems. Building on this, his 2019 study investigated EEG signal processing techniques tailored for rehabilitation robots, offering promising solutions for stroke patients suffering from movement disabilities, a problem affecting millions globally. Together, these papers have accumulated over 30 citations, reflecting meaningful community engagement with his methods. His more recent work demonstrates a broadening research portfolio, exploring non-contact remote photoplethysmography (rPPG) through spatial-temporal attention networks for human status assessment, showcasing his growing interest in real-world, camera-based physiological monitoring — a timely and impactful direction.
Research Focus
Key Achievements
Top Papers
- 1
- 2Investigation of EEG signal processing for rehabilitation robot control14 citations · 2019
- 3