Amaan Haque

Vellore Institute of Technology University

Papers

Total Citations

H-Index

About

Amaan Haque is a rising researcher at the intersection of neuroscience and artificial intelligence, with a primary focus on Brain-Computer Interfaces (BCI) and robotic control systems. His most cited work, "A Deep Learning Approach for Robotic Arm Control using Brain-Computer Interface" (2020), has garnered 5 citations and represents a significant step toward non-invasive neural prosthetics. In this study, Haque pioneered the use of Motor Imagery (MI) based on Electroencephalography (EEG) signals to control robotic arm movements—specifically lifting and dropping actions—demonstrating how deep learning models can decode human intent directly from brain activity. This contribution bridges the gap between cognitive neuroscience and practical robotics, offering a framework for assistive technologies that could restore mobility to individuals with paralysis. Haque's work stands out for its emphasis on real-time, EEG-driven control, reducing reliance on invasive implants. While still early in his career, his research has already influenced discussions on human-machine symbiosis and the future of neuroprosthetics. For students and researchers exploring BCI or human-robot interaction, Haque's work provides a compelling foundation for understanding how deep learning can transform raw neural signals into actionable commands.