Data from: Multi-gesture drag-and-drop decoding in a 2D iBCI control task

Objective. Intracortical brain-computer interfaces (iBCIs) have
demonstrated the ability to enable point-and-click as well as
reach-and-grasp control for people with tetraplegia. However, few studies
have investigated iBCIs during long-duration discrete movements that would
enable common computer interactions such as ”click-and-hold” or
”drag-and-drop.” Approach. Here, we examined the performance of
multi-class and binary (attempt/no-attempt) classification of neural
activity in the left precentral gyrus of two BrainGate2 clinical trial
participants performing hand gestures for 1, 2, and 4 seconds in duration.
We then designed a novel ”latch decoder” that utilizes parallel
multi-class and binary decoding processes and evaluated its performance on
data from isolated sustained gesture attempts and a multi-gesture
drag-and-drop task. Main Results. Neural activity during sustained
gestures revealed a marked decrease in the discriminability of hand
gestures sustained beyond 1 second. Compared to standard direct decoding
methods, the latch decoder demonstrated substantial improvement in
decoding accuracy for gestures performed independently or in conjunction
with simultaneous 2D cursor control. Significance. This work highlights
the unique neurophysiological response patterns of sustained gesture
attempts in human motor cortex and demonstrates a promising decoding
approach that could enable individuals with tetraplegia to intuitively
control a wider range of consumer electronics using an iBCI.