Excited to release a preprint about MINT! MINT is a highly accurate & very general BCI decoder that uses statistical constraints inspired by emerging scientific observations regarding neural geometries. Co-authors John Cunningham, Qi Wang, @MarkChurchland https://t.co/vrjB6fNaAj
Excited to release a preprint about MINT! MINT is a highly accurate & very general BCI decoder that uses statistical constraints inspired by emerging scientific observations regarding neural geometries. Co-authors John Cunningham, Qi Wang, @MarkChurchland https://t.co/vrjB6fNaAj
I'm excited to announce our preprint for the Neuropixels 1.0-NHP probe. This represents a large-scale collaborative effort to develop high-density electrodes for neural recording in large animal models like nonhuman primates. https://t.co/z5hv70VZKc
Thoroughly enjoyed the talks by Dr. Riki Banerjee (@synchroninc), @CohenKarniLab, @MaromBikson, @Perkins_SM, & Mary Kate Dwyer in the Neural Eng session at the #NEBEC2022 meeting yesterday. They covered SoA neural electrodes, BCI decoders, TBI, & neuromod for PASC, aka long COVID
Check out the full video demo in which MINT estimates a subject’s neural state and decodes velocity as the subject navigates a virtual environment. Paper in preparation – stay tuned! (4/4)
https://t.co/l5FVWZBQyp
I designed a neural decode algorithm (MINT) with @MarkChurchland. Very excited to team up with @BlackrockNeuro_ who will use MINT to restore movement to patients with paralysis! Huge thanks to collaborators @schroeder_ke, John Cunningham, and Qi Wang. Video demo in thread (1/4)
We are teaming up with @Columbia's neural decoder MINT, which will be integrated into the MoveAgain software, enabling patients' thoughts to be translated more optimally into prosthetic movements: https://t.co/C6Lzm8MBMA
This assumption enables accurate real-time neural state estimation in a probabilistic framework and provides a straightforward, nonlinear mapping from neural state to intended movement. (3/4)
Pleased to see MINT on the road to someday helping patients. MINT is a novel BMI decoding algorithm that leverages recent findings regarding trajectory geometry. Congrats to @Perkins_SM, @schroeder_ke, J. Cunningham, Qi Wang. Thxs @chethan for NLB'21. Explanatory video to follow.
This is a great analysis of our paper! And I love the analogy of someone shouting in a foreign language to get out of the way of a flood. Decoders based upon incorrect models (the wrong lang.) might let you take action (run), but they're missing a lot of valuable info @Perkins_SM
So happy to see this out in the world! I had a lot of fun working on this project with my amazing colleagues @schroeder_ke, Qi Wang, and @MarkChurchland.