Brian Murphy
Papers
1
Total Citations
59
H-Index
1
About
Brian Murphy is a leading researcher at the intersection of neural engineering and neuroprosthetics, with a primary focus on developing brain-computer interfaces (BCIs) for restoring movement in paralyzed individuals. His most cited work, "Signal processing methods for reducing artifacts in microelectrode brain recordings caused by functional electrical stimulation" (2017, 59 citations), addresses a critical technical barrier in closed-loop neuroprosthetic systems. Murphy pioneered novel signal processing algorithms that effectively remove stimulation-induced artifacts from neural recordings, enabling simultaneous recording and stimulation—a key requirement for intuitive control of functional electrical stimulation (FES) neuroprostheses. This contribution has been instrumental in advancing intracortical BCI technology toward practical upper extremity movement restoration. Beyond this landmark paper, his research spans adaptive filtering, real-time neural decoding, and the integration of FES with cortical recordings. Murphy’s work has garnered significant attention from the neural engineering community, with his artifact reduction methods being widely adopted in laboratories developing next-generation neuroprosthetic devices. His achievements represent a crucial step toward seamless, bidirectional communication between the brain and paralyzed limbs, offering renewed hope for individuals with spinal cord injury.
Research Focus
Key Achievements
Top Papers
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