J. Joshua Alfred

Vellore Institute of Technology University

Papers

Total Citations

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About

J. Joshua Alfred is a rising researcher at the forefront of Brain-Computer Interface (BCI) technology, with a primary focus on decoding neural signals for advanced prosthetic control. His most-cited work, "BCI based Robotic Arm Control using MI-EEG and Spiking Neural Network" (2022), demonstrates a novel approach to translating Motor Imagery (MI) from electroencephalography (EEG) into precise robotic hand movements. By integrating spiking neural networks, Alfred’s research enables more natural, real-time control of artificial limbs for lifting and dropping objects, directly addressing critical challenges in assistive robotics. Though early in his career, his work has already garnered attention for its potential to restore motor function in individuals with paralysis. Alfred’s contributions lie at the intersection of computational neuroscience and rehabilitation engineering, pushing the boundaries of how neural circuits can be analyzed and stimulated. His innovative use of biologically plausible neural models marks him as a promising voice in the next generation of BCI researchers, with implications for both clinical neuroprosthetics and human-machine interaction.