PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

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
In: ICASSP’08: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (2008) IEEE , pp. 5208-5211.


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.

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EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
ID Code:5242
Deposited By:Maneesh Sahani
Deposited On:24 March 2009