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

Vaidya, Mukta; Flint, Robert D.; Wang, Po T.; Barry, Alexander; Li, Yongcheng; Ghassemi, Mohammad M.; Tomic, Goran; Yao, Jun; Carmona, Carolina; Mugler, Emily M.; Gallick, Sarah; Driver, Sangeeta; Brkić, Nenad; Ripley, David L.; Liu, Charles; Kamper, Derek G.; An, Hongyu; Slutzky, Marc W.

doi:10.1109/tnsre.2019.2912298

Cited by 17 publications

(15 citation statements)

References 36 publications

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“…Generally, we achieved highly accurate decoding of the continuous time course of the behavioral variables (movement and force). These results compared favorably with prior studies decoding finger movement kinematics (Acharya et al, 2010;Nakanishi et al, 2014;Xie et al, 2018) and isometric force (Pistohl et al, 2013;Chen et al, 2014;Flint et al, 2014;Vaidya et al, 2019). Importantly, there was no significant difference in our ability to decode force and movement across subjects, implying that any differences in cortical representations of force and movement were not simply expressions of a superior decoding of one or the other.…”

Section: Discussionsupporting

confidence: 72%

The Representation of Finger Movement and Force in Human Motor and Premotor Cortices

Flint

Tate²,

et al. 2020

eNeuro

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The ability to grasp and manipulate objects requires controlling both finger movement kinematics and isometric force in rapid succession. Previous work suggests that these behavioral modes are controlled separately, but it is unknown whether the cerebral cortex represents them differently. Here, we asked the question of how movement and force were represented cortically, when executed sequentially with the same finger. We recorded high-density electrocorticography (ECoG) from the motor and premotor cortices of seven human subjects performing a movement-force motor task. We decoded finger movement [0.7 6 0.3 fractional variance accounted for (FVAF)] and force (0.7 6 0.2 FVAF) with high accuracy, yet found different spatial representations. In addition, we used a stateof-the-art deep learning method to uncover smooth, repeatable trajectories through ECoG state space during the movement-force task. We also summarized ECoG across trials and participants by developing a new metric, the neural vector angle (NVA). Thus, state-space techniques can help to investigate broad cortical networks. Finally, we were able to classify the behavioral mode from neural signals with high accuracy (90 6 6%). Thus, finger movement and force appear to have distinct representations in motor/premotor cortices. These results inform our understanding of the neural control of movement, as well as the design of grasp brain-machine interfaces (BMIs).

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Section: Discussionsupporting

confidence: 72%

The Representation of Finger Movement and Force in Human Motor and Premotor Cortices

Flint

Tate²,

et al. 2020

eNeuro

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“…Our recent study on EEG from TBI patients with hemicraniectomy demonstrated that the high-γ over HAs was capable of effectively decoding the thumb flexion force (Vaidya et al, 2019). In this study, we demonstrated that high-γ FIGURE 4 | Bar graphs for showing the electrodes with significant correlation in three conditions (baseline, after ERASE with simulated EMG and after running conventional ICA).…”

Section: Discussionmentioning

confidence: 67%

“…Meanwhile, movement and force are strongly encoded in the high-γ band activity from brain signals (Crone et al, 1998;Mehring et al, 2003;Pfurtscheller et al, 2003;Miller et al, 2007a;Schalk et al, 2007;Flint et al, 2012aFlint et al, ,b, 2014Flint et al, , 2016. Traumatic brain injury (TBI) patients with hemicraniectomy may be a useful model for human electrophysiology with high bandwidth and spatiotemporal resolution (Voytek et al, 2010;Vaidya et al, 2019). In particular, substantial high-γ band power can be detected in these patients' electroencephalogram (EEG) due to the absence of the skull in the hemicraniectomy area (referred to as hEEG) (Dannhauer et al, 2011;Lanfer et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Refinement of High-Gamma EEG Features From TBI Patients With Hemicraniectomy Using an ICA Informed by Simulated Myoelectric Artifacts

Wang

Vaidya

et al. 2020

Front. Neurosci.

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Recent studies have shown the ability to record high-γ signals (80–160 Hz) in electroencephalogram (EEG) from traumatic brain injury (TBI) patients who have had hemicraniectomies. However, extraction of the movement-related high-γ remains challenging due to a confounding bandwidth overlap with surface electromyogram (EMG) artifacts related to facial and head movements. In our previous work, we described an augmented independent component analysis (ICA) approach for removal of EMG artifacts from EEG, and referred to as EMG Reduction by Adding Sources of EMG (ERASE). Here, we tested this algorithm on EEG recorded from six TBI patients with hemicraniectomies while they performed a thumb flexion task. ERASE removed a mean of 52 ± 12% (mean ± S.E.M) (maximum 73%) of EMG artifacts. In contrast, conventional ICA removed a mean of 27 ± 19% (mean ± S.E.M) of EMG artifacts from EEG. In particular, high-γ synchronization was significantly improved in the contralateral hand motor cortex area within the hemicraniectomy site after ERASE was applied. A more sophisticated measure of high-γ complexity is the fractal dimension (FD). Here, we computed the FD of EEG high-γ on each channel. Relative FD of high-γ was defined as that the FD in move state was subtracted by FD in idle state. We found relative FD of high-γ over hemicraniectomy after applying ERASE were strongly correlated to the amplitude of finger flexion force. Results showed that significant correlation coefficients across the electrodes related to thumb flexion averaged ~0.76, while the coefficients across the homologous electrodes in non-hemicraniectomy areas were nearly 0. After conventional ICA, a correlation between relative FD of high-γ and force remained high in both hemicraniectomy areas (up to 0.86) and non-hemicraniectomy areas (up to 0.81). Across all subjects, an average of 83% of electrodes significantly correlated with force was located in the hemicraniectomy areas after applying ERASE. After conventional ICA, only 19% of electrodes with significant correlations were located in the hemicraniectomy. These results indicated that the new approach isolated electrophysiological features during finger motor activation while selectively removing confounding EMG artifacts. This approach removed EMG artifacts that can contaminate high-gamma activity recorded over the hemicraniectomy.

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“…Hence, ERASE can minimize the biases of researchers and improve the efficiency of artifacts rejection. As mentioned in the introduction, automated rejection is not necessarily unique to ERASE (Delorme et al, 2001 , 2007 ; Nicolaou and Nasuto, 2007 ; Nolan et al, 2010 ; Mognon et al, 2011 ; Daly et al, 2012 , 2013 ; Wu et al, 2018 ; Vaidya et al, 2019 ), given that other methods, such as cICA can also involve automatic IC rejection when prior knowledge of EMG signals is available (Hesse and James, 2006 ; Akhtar et al, 2012 ; Urigüen and Garcia-Zapirain, 2015 ). Previously reported EMG artifacts removal methods also proposed automated rejection techniques, in which some classifiers were built to classify the ICs into EMG sources and EEG sources based on ICs statistical features (Nolan et al, 2010 ; Gabsteiger et al, 2014 ; Wu et al, 2018 ).…”

Section: Discussionmentioning

confidence: 99%

Electromyogram (EMG) Removal by Adding Sources of EMG (ERASE)—A Novel ICA-Based Algorithm for Removing Myoelectric Artifacts From EEG

Wang

Vaidya

et al. 2021

Front. Neurosci.

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Electroencephalographic (EEG) recordings are often contaminated by electromyographic (EMG) artifacts, especially when recording during movement. Existing methods to remove EMG artifacts include independent component analysis (ICA), and other high-order statistical methods. However, these methods can not effectively remove most of EMG artifacts. Here, we proposed a modified ICA model for EMG artifacts removal in the EEG, which is called EMG Removal by Adding Sources of EMG (ERASE). In this new approach, additional channels of real EMG from neck and head muscles (reference artifacts) were added as inputs to ICA in order to “force” the most power from EMG artifacts into a few independent components (ICs). The ICs containing EMG artifacts (the “artifact ICs”) were identified and rejected using an automated procedure. ERASE was validated first using both simulated and experimentally-recorded EEG and EMG. Simulation results showed ERASE removed EMG artifacts from EEG significantly more effectively than conventional ICA. Also, it had a low false positive rate and high sensitivity. Subsequently, EEG was collected from 8 healthy participants while they moved their hands to test the realistic efficacy of this approach. Results showed that ERASE successfully removed EMG artifacts (on average, about 75% of EMG artifacts were removed when using real EMGs as reference artifacts) while preserving the expected EEG features related to movement. We also tested the ERASE procedure using simulated EMGs as reference artifacts (about 63% of EMG artifacts removed). Compared to conventional ICA, ERASE removed on average 26% more EMG artifacts from EEG. These findings suggest that ERASE can achieve significant separation of EEG signal and EMG artifacts without a loss of the underlying EEG features. These results indicate that using additional real or simulated EMG sources can increase the effectiveness of ICA in removing EMG artifacts from EEG. Combined with automated artifact IC rejection, ERASE also minimizes potential user bias. Future work will focus on improving ERASE so that it can also be used in real-time applications.

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

Cited by 17 publications

References 36 publications

The Representation of Finger Movement and Force in Human Motor and Premotor Cortices

The Representation of Finger Movement and Force in Human Motor and Premotor Cortices

Refinement of High-Gamma EEG Features From TBI Patients With Hemicraniectomy Using an ICA Informed by Simulated Myoelectric Artifacts

Electromyogram (EMG) Removal by Adding Sources of EMG (ERASE)—A Novel ICA-Based Algorithm for Removing Myoelectric Artifacts From EEG

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