Hemicraniectomy in Traumatic Brain Injury: A Noninvasive Platform to Investigate High Gamma Activity for Brain Machine Interfaces

Mukta Vaidya*, Robert Davisson Flint, Po T. Wang, Alex Barry, Yongcheng Li, Mohammad Ghassemi, Goran Tomic, Jun Yao, Carolina Carmona, Emily M. Mugler, Sarah Gallick, Sangeeta Driver, Nenad N Brkic, David Lawrence Ripley, Charles Liu, Derek Kamper, An H. Do, Marc W Slutzky

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

Brain-machine interfaces (BMIs) translate brain signals into control signals for an external device, such as a computer cursor or robotic limb. These signals can be obtained either noninvasively or invasively. Invasive recordings, using electrocorticography (ECoG) or intracortical microelectrodes, provide higher bandwidth and more informative signals. Rehabilitative BMIs, which aim to drive plasticity in the brain to enhance recovery after brain injury, have almost exclusively used non-invasive recordings, such electroencephalography (EEG) or magnetoencephalography (MEG), which have limited bandwidth and information content. Invasive recordings provide more information and spatiotemporal resolution, but do incur risk, and thus are not usually investigated in people with stroke or traumatic brain injury (TBI). Here, in this paper, we describe a new BMI paradigm to investigate the use of higher frequency signals in brain-injured subjects without incurring significant risk. We recorded EEG in TBI subjects who required hemicraniectomies (removal of a part of the skull). EEG over the hemicraniectomy (hEEG) contained substantial information in the high gamma frequency range (65-115 Hz). Using this information, we decoded continuous finger flexion force with moderate to high accuracy (variance accounted for 0.06 to 0.52), which at best approaches that using epidural signals. These results indicate that people with hemicraniectomies can provide a useful resource for developing BMI therapies for the treatment of brain injury.

Original languageEnglish (US)
Article number8697146
Pages (from-to)1467-1472
Number of pages6
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume27
Issue number7
DOIs
StatePublished - Jul 1 2019

Fingerprint

Brain-Computer Interfaces
Brain
Electroencephalography
Brain Injuries
Magnetoencephalography
Robotics
Microelectrodes
Skull
Fingers
Extremities
Stroke
Equipment and Supplies
Traumatic Brain Injury
Bandwidth
Plasticity

Keywords

  • brain-computer interface
  • Brain-machine interface
  • EEG
  • high gamma
  • traumatic brain injury

ASJC Scopus subject areas

  • Neuroscience(all)
  • Biomedical Engineering
  • Computer Science Applications

Cite this

Vaidya, Mukta ; Flint, Robert Davisson ; Wang, Po T. ; Barry, Alex ; Li, Yongcheng ; Ghassemi, Mohammad ; Tomic, Goran ; Yao, Jun ; Carmona, Carolina ; Mugler, Emily M. ; Gallick, Sarah ; Driver, Sangeeta ; Brkic, Nenad N ; Ripley, David Lawrence ; Liu, Charles ; Kamper, Derek ; Do, An H. ; Slutzky, Marc W. / Hemicraniectomy in Traumatic Brain Injury : A Noninvasive Platform to Investigate High Gamma Activity for Brain Machine Interfaces. In: IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2019 ; Vol. 27, No. 7. pp. 1467-1472.
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Hemicraniectomy in Traumatic Brain Injury : A Noninvasive Platform to Investigate High Gamma Activity for Brain Machine Interfaces. / Vaidya, Mukta; Flint, Robert Davisson; Wang, Po T.; Barry, Alex; Li, Yongcheng; Ghassemi, Mohammad; Tomic, Goran; Yao, Jun; Carmona, Carolina; Mugler, Emily M.; Gallick, Sarah; Driver, Sangeeta; Brkic, Nenad N; Ripley, David Lawrence; Liu, Charles; Kamper, Derek; Do, An H.; Slutzky, Marc W.

In: IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 27, No. 7, 8697146, 01.07.2019, p. 1467-1472.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Hemicraniectomy in Traumatic Brain Injury

T2 - A Noninvasive Platform to Investigate High Gamma Activity for Brain Machine Interfaces

AU - Vaidya, Mukta

AU - Flint, Robert Davisson

AU - Wang, Po T.

AU - Barry, Alex

AU - Li, Yongcheng

AU - Ghassemi, Mohammad

AU - Tomic, Goran

AU - Yao, Jun

AU - Carmona, Carolina

AU - Mugler, Emily M.

AU - Gallick, Sarah

AU - Driver, Sangeeta

AU - Brkic, Nenad N

AU - Ripley, David Lawrence

AU - Liu, Charles

AU - Kamper, Derek

AU - Do, An H.

AU - Slutzky, Marc W

PY - 2019/7/1

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N2 - Brain-machine interfaces (BMIs) translate brain signals into control signals for an external device, such as a computer cursor or robotic limb. These signals can be obtained either noninvasively or invasively. Invasive recordings, using electrocorticography (ECoG) or intracortical microelectrodes, provide higher bandwidth and more informative signals. Rehabilitative BMIs, which aim to drive plasticity in the brain to enhance recovery after brain injury, have almost exclusively used non-invasive recordings, such electroencephalography (EEG) or magnetoencephalography (MEG), which have limited bandwidth and information content. Invasive recordings provide more information and spatiotemporal resolution, but do incur risk, and thus are not usually investigated in people with stroke or traumatic brain injury (TBI). Here, in this paper, we describe a new BMI paradigm to investigate the use of higher frequency signals in brain-injured subjects without incurring significant risk. We recorded EEG in TBI subjects who required hemicraniectomies (removal of a part of the skull). EEG over the hemicraniectomy (hEEG) contained substantial information in the high gamma frequency range (65-115 Hz). Using this information, we decoded continuous finger flexion force with moderate to high accuracy (variance accounted for 0.06 to 0.52), which at best approaches that using epidural signals. These results indicate that people with hemicraniectomies can provide a useful resource for developing BMI therapies for the treatment of brain injury.

AB - Brain-machine interfaces (BMIs) translate brain signals into control signals for an external device, such as a computer cursor or robotic limb. These signals can be obtained either noninvasively or invasively. Invasive recordings, using electrocorticography (ECoG) or intracortical microelectrodes, provide higher bandwidth and more informative signals. Rehabilitative BMIs, which aim to drive plasticity in the brain to enhance recovery after brain injury, have almost exclusively used non-invasive recordings, such electroencephalography (EEG) or magnetoencephalography (MEG), which have limited bandwidth and information content. Invasive recordings provide more information and spatiotemporal resolution, but do incur risk, and thus are not usually investigated in people with stroke or traumatic brain injury (TBI). Here, in this paper, we describe a new BMI paradigm to investigate the use of higher frequency signals in brain-injured subjects without incurring significant risk. We recorded EEG in TBI subjects who required hemicraniectomies (removal of a part of the skull). EEG over the hemicraniectomy (hEEG) contained substantial information in the high gamma frequency range (65-115 Hz). Using this information, we decoded continuous finger flexion force with moderate to high accuracy (variance accounted for 0.06 to 0.52), which at best approaches that using epidural signals. These results indicate that people with hemicraniectomies can provide a useful resource for developing BMI therapies for the treatment of brain injury.

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KW - Brain-machine interface

KW - EEG

KW - high gamma

KW - traumatic brain injury

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