Neurologic Manifestations of Long COVID Differ Based on Acute COVID-19 Severity.

OBJECTIVE: To characterize neurologic manifestations in post-hospitalization Neuro-PASC (PNP) and non-hospitalized Neuro-PASC (NNP) patients. METHODS: Prospective study of the first 100 consecutive PNP and 500 NNP patients evaluated at a Neuro-COVID-19 clinic between 5/2020 and 8/2021. RESULTS: PNP were older than NNP patients (mean 53.9 vs 44.9 y; p < 0.0001) with a higher prevalence of pre-existing comorbidities. An average 6.8 months from onset, the main neurologic symptoms were "brain fog" (81.2%), headache (70.3%), and dizziness (49.5%) with only anosmia, dysgeusia and myalgias being more frequent in the NNP compared to the PNP group (59 vs 39%, 57.6 vs 39% and 50.4 vs 33%, all p < 0.003). Moreover, 85.8% of patients experienced fatigue. PNP more frequently had an abnormal neurologic exam than NNP patients (62.2 vs 37%, p < 0.0001). Both groups had impaired quality of life in cognitive, fatigue, sleep, anxiety, and depression domains. PNP patients performed worse on processing speed, attention, and working memory tasks than NNP patients (T-score 41.5 vs 55, 42.5 vs 47 and 45.5 vs 49, all p < 0.001) and a US normative population. NNP patients had lower results in attention task only. Subjective impression of cognitive ability correlated with cognitive test results in NNP but not in PNP patients. INTERPRETATION: PNP and NNP patients both experience persistent neurologic symptoms affecting their quality of life. However, they harbor significant differences in demographics, comorbidities, neurologic symptoms and findings, as well as pattern of cognitive dysfunction. Such differences suggest distinct etiologies of Neuro-PASC in these populations warranting targeted interventions. ANN NEUROL 2023;94:146-159.

Myoelectric interface for neurorehabilitation conditioning to reduce abnormal leg co-activation after stroke: a pilot study.

BACKGROUND: The ability to walk is an important factor in quality of life after stroke. Co-activation of hip adductors and knee extensors has been shown to correlate with gait impairment. We have shown previously that training with a myoelectric interface for neurorehabilitation (MINT) can reduce abnormal muscle co-activation in the arms of stroke survivors. METHODS: Here, we extend MINT conditioning to stroke survivors with leg impairment. The aim of this pilot study was to assess the safety and feasibility of using MINT to reduce abnormal co-activation between hip adductors and knee extensors and assess any effects on gait. Nine stroke survivors with moderate to severe gait impairment received 6 h of MINT conditioning over six sessions, either in the laboratory or at home. RESULTS: MINT participants completed a mean of 159 repetitions per session without any adverse events. Further, participants learned to isolate their muscles effectively, resulting in a mean reduction of co-activation of 70% compared to baseline. Moreover, gait speed increased by a mean of 0.15 m/s, more than the minimum clinically important difference. Knee flexion angle increased substantially, and hip circumduction decreased. CONCLUSION: MINT conditioning is safe, feasible at home, and enables reduction of co-activation in the leg. Further investigation of MINT's potential to improve leg movement and function after stroke is warranted. Abnormal co-activation of hip adductors and knee extensors may contribute to impaired gait after stroke. Trial registration This study was registered at ClinicalTrials.gov (NCT03401762, Registered 15 January 2018, https://clinicaltrials.gov/study/NCT03401762?tab=history&a=4 ).

Memory Reactivation during Sleep Improves Execution of a Challenging Motor Skill.

Memory reactivation during sleep reinforces various types of learning. Basic motor skills likely benefit from sleep. There is insufficient evidence, however, on whether memory reactivation during sleep contributes to learning how to execute a novel action. Here, we investigated motor learning in a myoelectric feedback task. Human male and female participants learned to control myoelectric activity in specific arm muscles to move a computer cursor to each of 16 locations. Each location was associated with a unique sound. Half of the sounds were played during slow-wave sleep to reactivate corresponding memories of muscle control. After sleep, movements cued during sleep were performed more quickly and efficiently than uncued movements. These results demonstrated that memory reactivation during sleep contributes to learning of action execution. We conclude that sleep supports learning novel actions, which also maps onto the learning required in certain neurorehabilitation procedures.SIGNIFICANCE STATEMENT Prior literature on motor learning has produced much evidence supporting a role for sleep but scant evidence on the execution component. This aspect of learning is critical for many complex skills that people value in their lives. Our results not only implicate sleep in skill learning but also pinpoint a benefit for motor execution using a method for modifying memory storage during sleep. We used targeted memory reactivation (TMR), whereby a stimulus that has been associated with learning is presented again during sleep to bring on a recapitulation of waking brain activity. Our demonstration that memory reactivation contributed to skilled performance may be relevant for neurorehabilitation as well as fields concerned with motor learning, such as kinesiology and physiology.

Myoelectric interface training enables targeted reduction in abnormal muscle co-activation.

BACKGROUND: Abnormal patterns of muscle co-activation contribute to impaired movement after stroke. Previously, we developed a myoelectric computer interface (MyoCI) training paradigm to improve stroke-induced arm motor impairment by reducing the abnormal co-activation of arm muscle pairs. However, it is unclear to what extent the paradigm induced changes in the overall intermuscular coordination in the arm, as opposed to changing just the muscles trained with the MyoCI. This study examined the intermuscular coordination patterns of thirty-two stroke survivors who participated in 6 weeks of MyoCI training. METHODS: We used non-negative matrix factorization to identify the arm muscle synergies (coordinated patterns of muscle activity) during a reaching task before and after the training. We examined the extent to which synergies changed as the training reduced motor impairment. In addition, we introduced a new synergy analysis metric, disparity index (DI), to capture the changes in the individual muscle weights within a synergy. RESULTS: There was no consistent pattern of change in the number of synergies across the subjects after the training. The composition of muscle synergies, calculated using a traditional synergy similarity metric, also did not change after the training. However, the disparity of muscle weights within synergies increased after the training in the participants who responded to MyoCI training-that is, the specific muscles that the MyoCI was targeting became less correlated within a synergy. This trend was not observed in participants who did not respond to the training. CONCLUSIONS: These findings suggest that MyoCI training reduced arm impairment by decoupling only the muscles trained while leaving other muscles relatively unaffected. This suggests that, even after injury, the nervous system is capable of motor learning on a highly fractionated level. It also suggests that MyoCI training can do what it was designed to do-enable stroke survivors to reduce abnormal co-activation in targeted muscles. Trial registration This study was registered at ClinicalTrials.gov (NCT03579992, Registered 09 July 2018-Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT03579992?term=NCT03579992&draw=2&rank=1 ).

Differential Representation of Articulatory Gestures and Phonemes in Precentral and Inferior Frontal Gyri.

Speech is a critical form of human communication and is central to our daily lives. Yet, despite decades of study, an understanding of the fundamental neural control of speech production remains incomplete. Current theories model speech production as a hierarchy from sentences and phrases down to words, syllables, speech sounds (phonemes), and the actions of vocal tract articulators used to produce speech sounds (articulatory gestures). Here, we investigate the cortical representation of articulatory gestures and phonemes in ventral precentral and inferior frontal gyri in men and women. Our results indicate that ventral precentral cortex represents gestures to a greater extent than phonemes, while inferior frontal cortex represents both gestures and phonemes. These findings suggest that speech production shares a common cortical representation with that of other types of movement, such as arm and hand movements. This has important implications both for our understanding of speech production and for the design of brain-machine interfaces to restore communication to people who cannot speak.SIGNIFICANCE STATEMENT Despite being studied for decades, the production of speech by the brain is not fully understood. In particular, the most elemental parts of speech, speech sounds (phonemes) and the movements of vocal tract articulators used to produce these sounds (articulatory gestures), have both been hypothesized to be encoded in motor cortex. Using direct cortical recordings, we found evidence that primary motor and premotor cortices represent gestures to a greater extent than phonemes. Inferior frontal cortex (part of Broca's area) appears to represent both gestures and phonemes. These findings suggest that speech production shares a similar cortical organizational structure with the movement of other body parts.

Long-Term Stability of Motor Cortical Activity: Implications for Brain Machine Interfaces and Optimal Feedback Control.

The human motor system is capable of remarkably precise control of movements--consider the skill of professional baseball pitchers or surgeons. This precise control relies upon stable representations of movements in the brain. Here, we investigated the stability of cortical activity at multiple spatial and temporal scales by recording local field potentials (LFPs) and action potentials (multiunit spikes, MSPs) while two monkeys controlled a cursor either with their hand or directly from the brain using a brain-machine interface. LFPs and some MSPs were remarkably stable over time periods ranging from 3 d to over 3 years; overall, LFPs were significantly more stable than spikes. We then assessed whether the stability of all neural activity, or just a subset of activity, was necessary to achieve stable behavior. We showed that projections of neural activity into the subspace relevant to the task (the "task-relevant space") were significantly more stable than were projections into the task-irrelevant (or "task-null") space. This provides cortical evidence in support of the minimum intervention principle, which proposes that optimal feedback control (OFC) allows the brain to tightly control only activity in the task-relevant space while allowing activity in the task-irrelevant space to vary substantially from trial to trial. We found that the brain appears capable of maintaining stable movement representations for extremely long periods of time, particularly so for neural activity in the task-relevant space, which agrees with OFC predictions. SIGNIFICANCE STATEMENT: It is unknown whether cortical signals are stable for more than a few weeks. Here, we demonstrate that motor cortical signals can exhibit high stability over several years. This result is particularly important to brain-machine interfaces because it could enable stable performance with infrequent recalibration. Although we can maintain movement accuracy over time, movement components that are unrelated to the goals of a task (such as elbow position during reaching) often vary from trial to trial. This is consistent with the minimum intervention principle of optimal feedback control. We provide evidence that the motor cortex acts according to this principle: cortical activity is more stable in the task-relevant space and more variable in the task-irrelevant space.

Physiological properties of brain-machine interface input signals.

Brain-machine interfaces (BMIs), also called brain-computer interfaces (BCIs), decode neural signals and use them to control some type of external device. Despite many experimental successes and terrific demonstrations in animals and humans, a high-performance, clinically viable device has not yet been developed for widespread usage. There are many factors that impact clinical viability and BMI performance. Arguably, the first of these is the selection of brain signals used to control BMIs. In this review, we summarize the physiological characteristics and performance-including movement-related information, longevity, and stability-of multiple types of input signals that have been used in invasive BMIs to date. These include intracortical spikes as well as field potentials obtained inside the cortex, at the surface of the cortex (electrocorticography), and at the surface of the dura mater (epidural signals). We also discuss the potential for future enhancements in input signal performance, both by improving hardware and by leveraging the knowledge of the physiological characteristics of these signals to improve decoding and stability.

Brain-machine interfaces in neurorehabilitation of stroke.

Stroke is among the leading causes of long-term disabilities leaving an increasing number of people with cognitive, affective and motor impairments depending on assistance in their daily life. While function after stroke can significantly improve in the first weeks and months, further recovery is often slow or non-existent in the more severe cases encompassing 30-50% of all stroke victims. The neurobiological mechanisms underlying recovery in those patients are incompletely understood. However, recent studies demonstrated the brain's remarkable capacity for functional and structural plasticity and recovery even in severe chronic stroke. As all established rehabilitation strategies require some remaining motor function, there is currently no standardized and accepted treatment for patients with complete chronic muscle paralysis. The development of brain-machine interfaces (BMIs) that translate brain activity into control signals of computers or external devices provides two new strategies to overcome stroke-related motor paralysis. First, BMIs can establish continuous high-dimensional brain-control of robotic devices or functional electric stimulation (FES) to assist in daily life activities (assistive BMI). Second, BMIs could facilitate neuroplasticity, thus enhancing motor learning and motor recovery (rehabilitative BMI). Advances in sensor technology, development of non-invasive and implantable wireless BMI-systems and their combination with brain stimulation, along with evidence for BMI systems' clinical efficacy suggest that BMI-related strategies will play an increasing role in neurorehabilitation of stroke.

Extracting kinetic information from human motor cortical signals.

Brain machine interfaces (BMIs) have the potential to provide intuitive control of neuroprostheses to restore grasp to patients with paralyzed or amputated upper limbs. For these neuroprostheses to function, the ability to accurately control grasp force is critical. Grasp force can be decoded from neuronal spikes in monkeys, and hand kinematics can be decoded using electrocorticogram (ECoG) signals recorded from the surface of the human motor cortex. We hypothesized that kinetic information about grasping could also be extracted from ECoG, and sought to decode continuously-graded grasp force. In this study, we decoded isometric pinch force with high accuracy from ECoG in 10 human subjects. The predicted signals explained from 22% to 88% (60 ± 6%, mean ± SE) of the variance in the actual force generated. We also decoded muscle activity in the finger flexors, with similar accuracy to force decoding. We found that high gamma band and time domain features of the ECoG signal were most informative about kinetics, similar to our previous findings with intracortical LFPs. In addition, we found that peak cortical representations of force applied by the index and little fingers were separated by only about 4mm. Thus, ECoG can be used to decode not only kinematics, but also kinetics of movement. This is an important step toward restoring intuitively-controlled grasp to impaired patients.

High-Gamma Activity Is Coupled to Low-Gamma Oscillations in Precentral Cortices and Modulates with Movement and Speech.

Planning and executing motor behaviors requires coordinated neural activity among multiple cortical and subcortical regions of the brain. Phase-amplitude coupling between the high-gamma band amplitude and the phase of low frequency oscillations (theta, alpha, beta) has been proposed to reflect neural communication, as has synchronization of low-gamma oscillations. However, coupling between low-gamma and high-gamma bands has not been investigated. Here, we measured phase-amplitude coupling between low- and high-gamma in monkeys performing a reaching task and in humans either performing finger-flexion or word-reading tasks. We found significant coupling between low-gamma phase and high-gamma amplitude in multiple sensorimotor and premotor cortices of both species during all tasks. This coupling modulated with the onset of movement. These findings suggest that interactions between the low and high gamma bands are markers of network dynamics related to movement and speech generation.

Myoelectric interface for neurorehabilitation conditioning to reduce abnormal leg co-activation after stroke: a pilot study.

Background: The ability to walk is an important factor in quality of life after stroke. Co-activation of hip adductors and knee extensors has been shown to correlate with gait impairment. We have shown previously that training with a myoelectric interface for neurorehabilitation (MINT) can reduce abnormal muscle co-activation in the arms of stroke survivors. Methods: Here, we extend MINT conditioning to stroke survivors with leg impairment. The aim of this pilot study was to assess the safety and feasibility of using MINT to reduce abnormal co-activation between hip adductors and knee extensors and assess any effects on gait. Nine stroke survivors with moderate to severe gait impairment received six hours of MINT conditioning over six sessions, either in the laboratory or at home. Results: MINT participants completed a mean of 159 repetitions per session without any adverse events. Further, participants learned to isolate their muscles effectively, resulting in a mean reduction of co-activation of 70% compared to baseline. Moreover, gait speed increased by a mean of 0.15 m/s, more than the minimum clinically important difference. Knee flexion angle increased substantially, and hip circumduction decreased. Conclusion: MINT conditioning is safe, feasible at home, and enables reduction of co-activation in the leg. Further investigation of MINT's potential to improve leg movement and function after stroke is warranted. Abnormal co-activation of hip adductors and knee extensors may contribute to impaired gait after stroke. Trial registration: This study was registered at ClinicalTrials.gov (NCT03401762, Registered 15 January 2018, https://clinicaltrials.gov/study/NCT03401762?tab=history&a=4).

High-gamma activity is coupled to low-gamma oscillations in precentral cortices and modulates with movement and speech.

Planning and executing motor behaviors requires coordinated neural activity among multiple cortical and subcortical regions of the brain. Phase-amplitude coupling between the high-gamma band amplitude and the phase of low frequency oscillations (theta, alpha, beta) has been proposed to reflect neural communication, as has synchronization of low-gamma oscillations. However, coupling between low-gamma and high-gamma bands has not been investigated. Here, we measured phase-amplitude coupling between low- and high-gamma in monkeys performing a reaching task and in humans either performing finger movements or speaking words aloud. We found significant coupling between low-gamma phase and high-gamma amplitude in multiple sensorimotor and premotor cortices of both species during all tasks. This coupling modulated with the onset of movement. These findings suggest that interactions between the low and high gamma bands are markers of network dynamics related to movement and speech generation.

Local field potentials allow accurate decoding of muscle activity.

Local field potentials (LFPs) in primary motor cortex include significant information about reach target location and upper limb movement kinematics. Some evidence suggests that they may be a more robust, longer-lasting signal than action potentials (spikes). Here we assess whether LFPs can also be used to decode upper limb muscle activity, a complex movement-related signal. We record electromyograms from both proximal and distal upper limb muscles from monkeys performing a variety of reach-to-grasp and isometric wrist force tasks. We show that LFPs can be used to decode activity from both proximal and distal muscles with performance rivaling that of spikes. Thus, motor cortical LFPs include information about more aspects of movement than has been previously demonstrated. This provides further evidence suggesting that LFPs could provide a highly informative, long-lasting signal source for neural prostheses.

Noninvasively recorded high-gamma signals improve synchrony of force feedback in a novel neurorehabilitation brain-machine interface for brain injury.

Objective.Brain injury is the leading cause of long-term disability worldwide, often resulting in impaired hand function. Brain-machine interfaces (BMIs) offer a potential way to improve hand function. BMIs often target replacing lost function, but may also be employed in neurorehabilitation (nrBMI) by facilitating neural plasticity and functional recovery. Here, we report a novel nrBMI capable of acquiring high-γ(70-115 Hz) information through a unique post-traumatic brain injury (TBI) hemicraniectomy window model, and delivering sensory feedback that is synchronized with, and proportional to, intended grasp force.Approach. We developed the nrBMI to use electroencephalogram recorded over a hemicraniectomy (hEEG) in individuals with TBI. The nrBMI empowered users to exert continuous, proportional control of applied force, and provided continuous force feedback. We report the results of an initial testing group of three human participants with TBI, along with a control group of three skull- and motor-intact volunteers.Main results. All participants controlled the nrBMI successfully, with high initial success rates (2 of 6 participants) or performance that improved over time (4 of 6 participants). We observed high-γmodulation with force intent in hEEG but not skull-intact EEG. Most significantly, we found that high-γcontrol significantly improved the timing synchronization between neural modulation onset and nrBMI output/haptic feedback (compared to low-frequency nrBMI control).Significance. These proof-of-concept results show that high-γnrBMIs can be used by individuals with impaired ability to control force (without immediately resorting to invasive signals like electrocorticography). Of note, the nrBMI includes a parameter to change the fraction of control shared between decoded intent and volitional force, to adjust for recovery progress. The improved synchrony between neural modulations and force control for high-γsignals is potentially important for maximizing the ability of nrBMIs to induce plasticity in neural circuits. Inducing plasticity is critical to functional recovery after brain injury.

Statistical assessment of the stability of neural movement representations.

In systems neuroscience, neural activity that represents movements or sensory stimuli is often characterized by spatial tuning curves that may change in response to training, attention, altered mechanics, or the passage of time. A vital step in determining whether tuning curves change is accounting for estimation uncertainty due to measurement noise. In this study, we address the issue of tuning curve stability using methods that take uncertainty directly into account. We analyze data recorded from neurons in primary motor cortex using chronically implanted, multielectrode arrays in four monkeys performing center-out reaching. With the use of simulations, we demonstrate that under typical experimental conditions, the effect of neuronal noise on estimated preferred direction can be quite large and is affected by both the amount of data and the modulation depth of the neurons. In experimental data, we find that after taking uncertainty into account using bootstrapping techniques, the majority of neurons appears to be very stable on a timescale of minutes to hours. Lastly, we introduce adaptive filtering methods to explicitly model dynamic tuning curves. In contrast to several previous findings suggesting that tuning curves may be in constant flux, we conclude that the neural representation of limb movement is, on average, quite stable and that impressions to the contrary may be largely the result of measurement noise.

Portable, open-source solutions for estimating wrist position during reaching in people with stroke.

Arm movement kinematics may provide a more sensitive way to assess neurorehabilitation outcomes than existing metrics. However, measuring arm kinematics in people with stroke can be challenging for traditional optical tracking systems due to non-ideal environments, expense, and difficulty performing required calibration. Here, we present two open-source methods, one using inertial measurement units (IMUs) and another using virtual reality (Vive) sensors, for accurate measurements of wrist position with respect to the shoulder during reaching movements in people with stroke. We assessed the accuracy of each method during a 3D reaching task. We also demonstrated each method's ability to track two metrics derived from kinematics-sweep area and smoothness-in people with chronic stroke. We computed correlation coefficients between the kinematics estimated by each method when appropriate. Compared to a traditional optical tracking system, both methods accurately tracked the wrist during reaching, with mean signed errors of 0.09 ± 1.81 cm and 0.48 ± 1.58 cm for the IMUs and Vive, respectively. Furthermore, both methods' estimated kinematics were highly correlated with each other (p < 0.01). By using relatively inexpensive wearable sensors, these methods may be useful for developing kinematic metrics to evaluate stroke rehabilitation outcomes in both laboratory and clinical environments.

Wearable myoelectric interface enables high-dose, home-based training in severely impaired chronic stroke survivors.

BACKGROUND: High-intensity occupational therapy can improve arm function after stroke, but many people lack access to such therapy. Home-based therapies could address this need, but they don't typically address abnormal muscle co-activation, an important aspect of arm impairment. An earlier study using lab-based, myoelectric computer interface game training enabled chronic stroke survivors to reduce abnormal co-activation and improve arm function. Here, we assess feasibility of doing this training at home using a novel, wearable, myoelectric interface for neurorehabilitation training (MINT) paradigm. OBJECTIVE: Assess tolerability and feasibility of home-based, high-dose MINT therapy in severely impaired chronic stroke survivors. METHODS: Twenty-three participants were instructed to train with the MINT and game for 90 min/day, 36 days over 6 weeks. We assessed feasibility using amount of time trained and game performance. We assessed tolerability (enjoyment and effort) using a customized version of the Intrinsic Motivation Inventory at the conclusion of training. RESULTS: Participants displayed high adherence to near-daily therapy at home (mean of 82 min/day of training; 96% trained at least 60 min/day) and enjoyed the therapy. Training performance improved and co-activation decreased with training. Although a substantial number of participants stopped training, most dropouts were due to reasons unrelated to the training paradigm itself. INTERPRETATION: Home-based therapy with MINT is feasible and tolerable in severely impaired stroke survivors. This affordable, enjoyable, and mobile health paradigm has potential to improve recovery from stroke in a variety of settings. Clinicaltrials.gov: NCT03401762.

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

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.

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

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.