Literature DB >> 23027946

Cognitive signals for brain-machine interfaces in posterior parietal cortex include continuous 3D trajectory commands.

Markus Hauschild1, Grant H Mulliken, Igor Fineman, Gerald E Loeb, Richard A Andersen.   

Abstract

Cortical neural prosthetics extract command signals from the brain with the goal to restore function in paralyzed or amputated patients. Continuous control signals can be extracted from the motor cortical areas, whereas neural activity from posterior parietal cortex (PPC) can be used to decode cognitive variables related to the goals of movement. Because typical activities of daily living comprise both continuous control tasks such as reaching, and tasks benefiting from discrete control such as typing on a keyboard, availability of both signals simultaneously would promise significant increases in performance and versatility. Here, we show that PPC can provide 3D hand trajectory information under natural conditions that would be encountered for prosthetic applications, thus allowing simultaneous extraction of continuous and discrete signals without requiring multisite surgical implants. We found that limb movements can be decoded robustly and with high accuracy from a small population of neural units under free gaze in a complex 3D point-to-point reaching task. Both animals' brain-control performance improved rapidly with practice, resulting in faster target acquisition and increasing accuracy. These findings disprove the notion that the motor cortical areas are the only candidate areas for continuous prosthetic command signals and, rather, suggests that PPC can provide equally useful trajectory signals in addition to discrete, cognitive variables. Hybrid use of continuous and discrete signals from PPC may enable a new generation of neural prostheses providing superior performance and additional flexibility in addressing individual patient needs.

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Year:  2012        PMID: 23027946      PMCID: PMC3479517          DOI: 10.1073/pnas.1215092109

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  37 in total

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Review 4.  A computational neuroanatomy for motor control.

Authors:  Reza Shadmehr; John W Krakauer
Journal:  Exp Brain Res       Date:  2008-02-05       Impact factor: 1.972

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Journal:  Nature       Date:  2006-07-13       Impact factor: 49.962

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Journal:  Nature       Date:  2006-07-13       Impact factor: 49.962

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Journal:  Cereb Cortex       Date:  1994 Nov-Dec       Impact factor: 5.357

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Journal:  Nat Neurosci       Date:  1998-10       Impact factor: 24.884

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Journal:  Science       Date:  1996-09-13       Impact factor: 47.728

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  34 in total

1.  Parietal neural prosthetic control of a computer cursor in a graphical-user-interface task.

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Review 2.  The challenge of understanding the brain: where we stand in 2015.

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3.  Wireless recording from unrestrained monkeys reveals motor goal encoding beyond immediate reach in frontoparietal cortex.

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4.  Adaptive neuron-to-EMG decoder training for FES neuroprostheses.

Authors:  Christian Ethier; Daniel Acuna; Sara A Solla; Lee E Miller
Journal:  J Neural Eng       Date:  2016-06-01       Impact factor: 5.379

5.  Neurophysiology. Decoding motor imagery from the posterior parietal cortex of a tetraplegic human.

Authors:  Tyson Aflalo; Spencer Kellis; Christian Klaes; Brian Lee; Ying Shi; Kelsie Pejsa; Kathleen Shanfield; Stephanie Hayes-Jackson; Mindy Aisen; Christi Heck; Charles Liu; Richard A Andersen
Journal:  Science       Date:  2015-05-22       Impact factor: 47.728

6.  Spatiotemporal dynamics of online motor correction processing revealed by high-density electroencephalography.

Authors:  Laura Dipietro; Howard Poizner; Hermano I Krebs
Journal:  J Cogn Neurosci       Date:  2014-02-24       Impact factor: 3.225

Review 7.  Optic ataxia: from Balint's syndrome to the parietal reach region.

Authors:  Richard A Andersen; Kristen N Andersen; Eun Jung Hwang; Markus Hauschild
Journal:  Neuron       Date:  2014-03-05       Impact factor: 17.173

8.  Advantages of closed-loop calibration in intracortical brain-computer interfaces for people with tetraplegia.

Authors:  Beata Jarosiewicz; Nicolas Y Masse; Daniel Bacher; Sydney S Cash; Emad Eskandar; Gerhard Friehs; John P Donoghue; Leigh R Hochberg
Journal:  J Neural Eng       Date:  2013-07-10       Impact factor: 5.379

9.  A brain-machine interface enables bimanual arm movements in monkeys.

Authors:  Peter J Ifft; Solaiman Shokur; Zheng Li; Mikhail A Lebedev; Miguel A L Nicolelis
Journal:  Sci Transl Med       Date:  2013-11-06       Impact factor: 17.956

Review 10.  Brain-computer interfaces for dissecting cognitive processes underlying sensorimotor control.

Authors:  Matthew D Golub; Steven M Chase; Aaron P Batista; Byron M Yu
Journal:  Curr Opin Neurobiol       Date:  2016-01-19       Impact factor: 6.627

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