Papers
1
Total Citations
5
H-Index
1
About
Anuj Anand is a researcher at the forefront of Brain-Computer Interface (BCI) technology, specializing in the intersection of deep learning and robotic control. His primary research focuses on decoding neural signals—specifically Motor Imagery (MI) based on Electroencephalography (EEG)—to enable direct, non-invasive communication between the human brain and external devices. His most cited work, "A Deep Learning Approach for Robotic Arm Control using Brain-Computer Interface" (2020), presents a novel framework that translates EEG signals into precise commands for lifting and dropping a robotic arm. This contribution is pivotal for advancing assistive technologies, offering potential independence to individuals with motor disabilities. With 5 citations, his work is gaining traction as a foundational step toward practical, real-world BCI applications. Anand’s research not only demonstrates technical innovation in deep learning architectures but also addresses critical challenges in signal processing and real-time control, marking him as an emerging voice in the field of neural engineering and human-robot interaction.
Research Focus
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Top Papers
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