A factor-analysis decoder for high-performance neural prostheses.
Gopal Santhanam, Byron M. Yu, Vikash Gilja, Stephen I. Ryu, Afsheen Afshar, Maneesh Sahani and Krishna V. Shenoy
ICASSP’08: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing
Increasing the performance of neural prostheses is necessary
for assuring their clinical viability. One performance limitation is the presence of correlated trial-to-trial variability that
can cause neural responses to wax and wane in concert as
the subject is, for example, more attentive or more fatigued.
We report here the design and characterization of a Factor-Analysis-based decoding algorithm that is able to contend
with this confound. We characterize the decoder (classifier)
on a previously reported dataset where monkeys performed
both a real reach task and a prosthetic cursor movement task
while we recorded from 96 electrodes implanted in dorsal pre-motor cortex. In principle, the decoder infers the underlying
factors that co-modulate the neurons' responses and can use
this information to function with reduced error rates (1 of 8
reach target prediction) of up to 75% (20% total prediction error using independent Gaussian or Poisson models became 5%). Such Factor-Analysis based methods appear to
be effective when attempting to combat directly unobserved
trial-by-trial neural variabiliy.