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 language | English (US) |
|---|---|
| Article number | 8697146 |
| Pages (from-to) | 1467-1472 |
| Number of pages | 6 |
| Journal | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| Volume | 27 |
| Issue number | 7 |
| DOIs | |
| State | Published - Jul 1 2019 |
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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
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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 journal › Article
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
Y1 - 2019/7/1
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.
KW - brain-computer interface
KW - Brain-machine interface
KW - EEG
KW - high gamma
KW - traumatic brain injury
UR - http://www.scopus.com/inward/record.url?scp=85068724450&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85068724450&partnerID=8YFLogxK
U2 - 10.1109/TNSRE.2019.2912298
DO - 10.1109/TNSRE.2019.2912298
M3 - Article
C2 - 31021800
AN - SCOPUS:85068724450
VL - 27
SP - 1467
EP - 1472
JO - IEEE Transactions on Neural Systems and Rehabilitation Engineering
JF - IEEE Transactions on Neural Systems and Rehabilitation Engineering
SN - 1534-4320
IS - 7
M1 - 8697146
ER -