John Sahaya
Papers
1
Total Citations
5
H-Index
1
About
John Sahaya is a researcher at the intersection of artificial intelligence, brain-computer interfaces (BCI), and assistive robotics. His primary focus lies in decoding neural signals to enable intuitive control of external devices, with a particular emphasis on motor imagery (MI) based on electroencephalography (EEG). In his most cited work, "A Deep Learning Approach for Robotic Arm Control using Brain-Computer Interface" (2020, 5 citations), Sahaya developed a novel framework that translates EEG signals into commands for lifting and dropping a robotic arm. This contribution demonstrates how deep learning can bridge the gap between human intent and machine action, offering a pathway toward non-invasive, thought-controlled prosthetics. While his citation count is still growing, the work represents a meaningful step in making BCI technology more practical and accessible. Sahaya’s research holds promise for restoring motor function in individuals with paralysis or limb loss, and his approach highlights the potential of combining neural decoding with robotic actuation to create responsive, user-centered assistive technologies.
Research Focus
Key Achievements
Top Papers
- 1