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Author Santhanam, G. ♦ Yu, B. M. ♦ Gilja, V. ♦ Ryu, S. I. ♦ Afshar, A. ♦ Sahani, M. ♦ Shenoy, K. V.
Source CiteSeerX
Content type Text
File Format PDF
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Medical Scientist ♦ Neural Prosthesis ♦ Unobserved Trial-by-trial Neural Variabiliy ♦ Correlated Trial-to-trial Variability ♦ Dorsal Premotor Cortex ♦ Index Term Factor Analysis ♦ Premotor Cortex ♦ Prosthetic Cursor Movement Task ♦ Reduced Error Rate ♦ Clinical Viability ♦ Real Reach Task ♦ Reach Target Prediction ♦ Neuron Response ♦ Poisson Model ♦ Performance Limitation ♦ Total Prediction Error ♦ Brain-computer Interface ♦ Neural Response ♦ Factor-analysis-based Decoding Algorithm ♦ Underlying Factor
Abstract 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 ∼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. Index Terms — Factor analysis, premotor cortex, brainmachine and brain-computer interfaces, neural prostheses
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study