Dharmendra Gurve
Papers
3
Total Citations
137
H-Index
3
About
Dharmendra Gurve is a leading researcher in the field of brain-computer interfaces (BCIs), with a specific focus on lower-limb rehabilitation and assistive robotics. His work centers on decoding neural activity from electroencephalography (EEG) signals to enable intuitive control of external devices. Gurve’s major contributions include the development of novel, subject-specific signal processing techniques that dramatically improve the recognition of motor imagery. For instance, his 2019 paper on recognizing pedaling motor imagery (49 citations) introduced an unsupervised method for feature extraction, while his work on subject-specific EEG channel selection (48 citations) reduced computational load without sacrificing accuracy. He has also advanced practical applications, such as his 2022 study on a CCA-based compressive sensing framework for SSVEP-based BCIs (40 citations), which successfully commanded a robotic wheelchair. By tackling key challenges in real-time signal processing and channel optimization, Gurve’s research is paving the way for more accessible, efficient, and user-friendly BCI systems for individuals with severe motor disabilities.
Research Focus
Key Achievements
Top Papers
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