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State-Dependent Decoding Algorithms Improve the Performance of a Bidirectional BMI in Anesthetized Rats.

De Feo, Vito and Boi, Fabio and Safaai, Houman and Onken, Arno and Panzeri, Stefano and Vato, Alessandro (2017) 'State-Dependent Decoding Algorithms Improve the Performance of a Bidirectional BMI in Anesthetized Rats.' Frontiers in Neuroscience, 11 (MAY). 269-. ISSN 1662-453X

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Abstract

Brain-machine interfaces (BMIs) promise to improve the quality of life of patients suffering from sensory and motor disabilities by creating a direct communication channel between the brain and the external world. Yet, their performance is currently limited by the relatively small amount of information that can be decoded from neural activity recorded form the brain. We have recently proposed that such decoding performance may be improved when using state-dependent decoding algorithms that predict and discount the large component of the trial-to-trial variability of neural activity which is due to the dependence of neural responses on the network's current internal state. Here we tested this idea by using a bidirectional BMI to investigate the gain in performance arising from using a state-dependent decoding algorithm. This BMI, implemented in anesthetized rats, controlled the movement of a dynamical system using neural activity decoded from motor cortex and fed back to the brain the dynamical system's position by electrically microstimulating somatosensory cortex. We found that using state-dependent algorithms that tracked the dynamics of ongoing activity led to an increase in the amount of information extracted form neural activity by 22%, with a consequently increase in all of the indices measuring the BMI's performance in controlling the dynamical system. This suggests that state-dependent decoding algorithms may be used to enhance BMIs at moderate computational cost.

Item Type: Article
Uncontrolled Keywords: state dependence; brain-machine interfaces; neural coding; neural response variability; information coding; network state
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 07 Oct 2021 14:58
Last Modified: 23 Sep 2022 19:38
URI: http://repository.essex.ac.uk/id/eprint/29792

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