Matthew Tom Harrison

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About

Matthew Tom Harrison is a leading researcher in neural engineering, with a primary focus on intracortical brain–computer interfaces (iBCIs) for restoring motor function in individuals with paralysis. His work centers on improving the accuracy and reliability of decoding algorithms that translate neural activity into real-time control of assistive devices, such as robotic arms and computer cursors. Harrison’s most cited paper, “Adaptive Offset Correction for Intracortical Brain–Computer Interfaces” (2013, 21 citations), introduced a method to dynamically correct signal drift during iBCI operation, significantly enhancing long-term decoding stability. In another influential study, “Mixing decoded cursor velocity and position from an offline Kalman filter improves cursor control in people with tetraplegia” (2013, 13 citations), he demonstrated that combining velocity and position decoding within a Kalman filter framework substantially improved cursor control for users with tetraplegia. This work directly advanced the clinical viability of iBCIs, enabling more intuitive and precise device operation. Harrison’s contributions have been instrumental in bridging the gap between neural signal processing and practical assistive technology, with his research cited in over 30 publications. His innovative approaches continue to shape the development of next-generation brain–computer interfaces, offering renewed hope for individuals with severe motor disabilities.