Data from: Measuring instability in chronic human intracortical neural recordings towards stable, long-term brain-computer interfaces

Intracortical brain-computer interfaces (iBCIs) enable people with
tetraplegia to gain intuitive cursor control from movement intentions. To
translate to practical use, iBCIs should provide reliable performance for
extended periods of time. However, performance begins to degrade as the
relationship between kinematic intention and recorded neural activity
shifts compared to when the decoder was initially trained. In addition to
developing decoders to better handle long-term instability, identifying
when to recalibrate will also optimize performance. We propose a method,
“MINDFUL”, to measure instabilities in neural data for useful long-term
iBCI, without needing labels of user intentions. Longitudinal
data were analyzed from two BrainGate2 participants with tetraplegia as
they used fixed decoders to control a computer cursor spanning 142 days
and 28 days, respectively. We demonstrate a measure of instability that
correlates with changes in closed-loop cursor performance solely based on
the recorded neural activity (Pearson r = 0.93 and 0.72,
respectively). This result suggests a strategy to infer online iBCI
performance from neural data alone and to determine when recalibration
should take place for practical long-term use.