A Factor-Analysis decoder for high-performance neural prostheses.
2008 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING, VOLS 1-12.
(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 premotor 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 similar to 75% (similar to 20% total prediction error using independent Gaussian or Poisson models became similar to 5%). Such Factor-Analysis based methods appear to be effective when attempting to combat directly unobserved trial-by-trial neural variabiliy.
|Title:||A Factor-Analysis decoder for high-performance neural prostheses|
|Event:||33rd IEEE International Conference on Acoustics, Speech and Signal Processing|
|Location:||Las Vegas, NV|
|Dates:||2008-03-30 - 2008-04-04|
|Keywords:||factor analysis, premotor cortex, brain-machine and brain-computer interfaces, neural prostheses|
|UCL classification:||UCL > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neuroscience Unit|
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