Matthew Willsey is originally from Greenwood, Indiana. He attended MIT, where he received B.S. and M.Eng. degrees in Electrical Engineering with a research focus in digital signal processing. He later attended medical school at Baylor College of Medicine in Houston and completed his neurosurgery residency at the University of Michigan in 2022. During his residency, he completed an enfolded CAST-approved fellowship in Stereotactic and Functional Neurosurgery. He is currently completing a post-graduate, one-year fellowship in stereotactic/functional neurosurgery and epilepsy at Stanford University under the direction of Dr. Jaimie Henderson. His clinical interests include deep brain stimulation, MR-guided focused ultrasound, epilepsy, pain, and spine.
His research has largely focused on neuromodulation and intracortical brain-computer interfaces (iBCI). He completed a PhD under Drs. Parag Patil and Cynthia Chestek during his resident research time plus an additional leave-of-absence year. He is currently working in the Neural Prosthetics Laboratories at Stanford where he has continued to investigate using iBCIs to restore fine motor control in human participants with paralysis enrolled in the BrainGate2 clinical trial.
Clinical Instructor, Neurosurgery
Residency: University of Michigan Dept of Neurosurgery (2022) MI
PhD, University of Michigan, Biomedical Engineering (2020)
Fellowship: University of Michigan Dept of Neurosurgery (2019) MI
Medical Education: Baylor College of Medicine (2014) TX
A low-power band of neuronal spiking activity dominated by local single units improves the performance of brain-machine interfaces.
Nature biomedical engineering
The large power requirement of current brain-machine interfaces is a major hindrance to their clinical translation. In basic behavioural tasks, the downsampled magnitude of the 300-1,000 Hz band of spiking activity can predict movement similarly to the threshold crossing rate (TCR) at 30 kilo-samples per second. However, the relationship between such a spiking-band power (SBP) and neural activity remains unclear, as does the capability of using the SBP to decode complicated behaviour. By using simulations of recordings of neural activity, here we show that the SBP is dominated by local single-unit spikes with spatial specificity comparable to or better than that of the TCR, and that the SBP correlates better with the firing rates of lower signal-to-noise-ratio units than the TCR. With non-human primates, in an online task involving the one-dimensional decoding of the movement of finger groups and in an offline two-dimensional cursor-control task, the SBP performed equally well or better than the TCR. The SBP may enhance the decoding performance of neural interfaces while enabling substantial cuts in power consumption.
View details for DOI 10.1038/s41551-020-0591-0
View details for PubMedID 32719512