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BCI decoding framework.

By Josh Merel (747586), Donald M. Pianto (747587), John P. Cunningham (747588) and Liam Paninski (155009)


<p>Top figure depicts the general setting where neural activity is taken together with prior information to decode an estimate of the underlying intention. When in closed loop, sensory (visual) feedback is provided which can allow the user to modify intention and also permit changes to the encoding model. Bottom figure depicts the steady state Kalman filter (SSKF), a simple exemplar of the general setting in which <i>A</i>, <i>F</i>, & <i>G</i> are matrices which multiply the vectors <i>x</i><sub><i>t</i></sub>, <i>y</i><sub><i>t</i></sub>, or </p><p></p><p></p><p></p><p><mi>x</mi><mo>^</mo></p><p><mi>t</mi><mo>−</mo><mn>1</mn></p><p></p><p></p><p></p>. Contributions from <i>Fy</i><sub><i>t</i></sub> and <p></p><p><mi>G</mi></p><p></p><p><mi>x</mi><mo>^</mo></p><p><mi>t</mi><mo>−</mo><mn>1</mn></p><p></p><p></p><p></p> combine additively.<p></p

Topics: Biological Sciences, Science Policy, BCI, decoding algorithm, performance advantages, end effector, prosthesis simulator, decoding system, computer screen, approach yields, user, experiments support, control signals, optimized decoder
Year: 2015
DOI identifier: 10.1371/journal.pcbi.1004288.g001
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Provided by: FigShare
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