Characterization of electrocorticogram high-gamma signal in response to varying upper extremity movement velocity

Artifact propagation in subdural cortical electrostimulation: Characterization and modeling

Cortical stimulation via electrocorticography (ECoG) may be an effective method for inducing artificial sensation in bi-directional brain-computer interfaces (BD-BCIs). However, strong electrical artifacts caused by electrostimulation may significantly degrade or obscure neural information. A detailed understanding of stimulation artifact propagation through relevant tissues may improve existing artifact suppression techniques or inspire the development of novel artifact mitigation strategies. Our work thus seeks to comprehensively characterize and model the propagation of artifacts in subdural ECoG stimulation. To this end, we collected and analyzed data from eloquent cortex mapping procedures of four subjects with epilepsy who were implanted with subdural ECoG electrodes. From this data, we observed that artifacts exhibited phase-locking and ratcheting characteristics in the time domain across all subjects. In the frequency domain, stimulation caused broadband power increases, as well as power bursts at the fundamental stimulation frequency and its super-harmonics. The spatial distribution of artifacts followed the potential distribution of an electric dipole with a median goodness-of-fit of R² = 0.80 across all subjects and stimulation channels. Artifacts as large as ±1,100 μV appeared anywhere from 4.43 to 38.34 mm from the stimulation channel. These temporal, spectral and spatial characteristics can be utilized to improve existing artifact suppression techniques, inspire new strategies for artifact mitigation, and aid in the development of novel cortical stimulation protocols. Taken together, these findings deepen our understanding of cortical electrostimulation and provide critical design specifications for future BD-BCI systems.

Modulation of Gamma Spectral Amplitude and Connectivity During Reaching Predicts Peak Velocity and Movement Duration

Modulation of gamma oscillations recorded from the human motor cortex and basal ganglia appears to play a key role in movement execution. However, there are still major questions to be answered about the specific role of cortical gamma activity in both the planning and execution of movement features such as the scaling of peak velocity and movement time. In this study, we characterized movement-related gamma oscillatory dynamics and its relationship with kinematic parameters based on 256-channels EEG recordings in 64 healthy subjects while performing fast and uncorrected reaching movements to targets located at three distances. In keeping with previous studies, we found that movement-related gamma synchronization occurred during movement execution. As a new finding, we showed that gamma synchronization occurred also before movement onset, with planning and execution phases involving different gamma peak frequencies and topographies. Importantly, the amplitude of gamma synchronization in both planning and execution increased with target distance and predicted peak velocity and movement time. Additional analysis of phase coherence revealed a gamma-coordinated long-range network involving occipital, frontal and central regions during movement execution that was positively related to kinematic features. This is the first evidence in humans supporting the notion that gamma synchronization amplitude and phase coherence pattern can reliably predict peak velocity amplitude and movement time. Therefore, these findings suggest that cortical gamma oscillations have a crucial role for the selection, implementation and control of the appropriate kinematic parameters of goal-directed reaching movements.

Frequency-Dependent Effects on Coordination and Prefrontal Hemodynamics During Finger Force Production Tasks

Behavioral stability partially depends on the variability of net outcomes by means of the co-varied adjustment of individual elements such as multi-finger forces. The properties of cyclic actions affect stability and variability of the performance as well as the activation of the prefrontal cortex that is an origin of subcortical structure for the coordinative actions. Little research has been done on the issue of the relationship between stability and neuronal response. The purpose of the study was to investigate the changes in the neural response, particularly at the prefrontal cortex, to the frequencies of isometric cyclic finger force production. The main experimental task was to produce finger forces while matching the produced force to sine-wave templates as accurately as possible. Also, the hemodynamics responses of the prefrontal cortex, including oxy-hemoglobin concentration (ΔHbO) and the functional connectivity, were measured using functional near-infrared spectroscopy. The frequency conditions comprised 0.1, 1, and 2 Hz. The uncontrolled manifold (UCM) approach was applied to compute synergy indices in time-series. The relative phase (RP), the coefficient of variation (CV) of the peak and trough force values were computed as the indices of performance accuracy. The statistical parametric mapping (SPM) was implemented to compare the synergy indices of three frequency conditions in time-series. A less accurate performance in the high-frequency condition was caused not by the RP, but mainly by the inconsistent peak force values (CV; p < 0.01, η p 2 = 0.90). The SPM analysis revealed that the synergy indices were larger in the low-frequency than in high-frequency conditions. Further, the ΔHbO remained unchanged under all frequency conditions, while the functional connectivity decreased with an increase in the frequency of cyclic force production. The current results suggested that the concurrent activation of the prefrontal region mainly depends on the frequency of cyclic force production, which was associated with the strength of stability indices and performance errors. The current study is the first work to uncover the effect of frequency on the multi-finger synergies as to the hemodynamic response in the prefrontal cortex, which possibly provides a clue of the neural mechanism of synergy formation and its changes.

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.

An Energy-Efficient CMOS Dual-Mode Array Architecture for High-Density ECoG-Based Brain-Machine Interfaces

This article presents an energy-efficient electrocorticography (ECoG) array architecture for fully-implantable brain machine interface systems. A novel dual-mode analog signal processing method is introduced that extracts neural features from high- γ band (80-160 Hz) at the early stages of signal acquisition. Initially, brain activity across the full-spectrum is momentarily observed to compute the feature weights in the digital back-end during full-band mode operation. Subsequently, these weights are fed back to the front-end and the system reverts to base-band mode to perform feature extraction. This approach utilizes a distinct optimized signal pathway based on power envelope extraction, resulting in 1.72× power reduction in the analog blocks and up to 50× potential power savings for digitization and processing (implemented off-chip in this article). A prototype incorporating a 32-channel ultra-low power signal acquisition front-end is fabricated in 180 nm CMOS process with 0.8 V supply. This chip consumes 1.05  μW (0.205  μW for feature extraction only) power and occupies 0.245 [Formula: see text] die area per channel. The chip measurement shows better than 76.5-dB common-mode rejection ratio (CMRR), 4.09 noise efficiency factor (NEF), and 10.04 power efficiency factor (PEF). In-vivo human tests have been carried out with electroencephalography and ECoG signals to validate the performance and dual-mode operation in comparison to commercial acquisition systems.

A benchtop system to assess the feasibility of a fully independent and implantable brain-machine interface

OBJECTIVE

State-of-the-art invasive brain-machine interfaces (BMIs) have shown significant promise, but rely on external electronics and wired connections between the brain and these external components. This configuration presents health risks and limits practical use. These limitations can be addressed by designing a fully implantable BMI similar to existing FDA-approved implantable devices. Here, a prototype BMI system whose size and power consumption are comparable to those of fully implantable medical devices was designed and implemented, and its performance was tested at the benchtop and bedside.

APPROACH

A prototype of a fully implantable BMI system was designed and implemented as a miniaturized embedded system. This benchtop analogue was tested in its ability to acquire signals, train a decoder, perform online decoding, wirelessly control external devices, and operate independently on battery. Furthermore, performance metrics such as power consumption were benchmarked.

MAIN RESULTS

An analogue of a fully implantable BMI was fabricated with a miniaturized form factor. A patient undergoing epilepsy surgery evaluation with an electrocorticogram (ECoG) grid implanted over the primary motor cortex was recruited to operate the system. Seven online runs were performed with an average binary state decoding accuracy of 87.0% (lag optimized, or 85.0% at fixed latency). The system was powered by a wirelessly rechargeable battery, consumed  ∼150 mW, and operated for  >60 h on a single battery cycle.

SIGNIFICANCE

The BMI analogue achieved immediate and accurate decoding of ECoG signals underlying hand movements. A wirelessly rechargeable battery and other supporting functions allowed the system to function independently. In addition to the small footprint and acceptable power and heat dissipation, these results suggest that fully implantable BMI systems are feasible.

A novel algorithm for removing artifacts from EEG data

In recent years, many studies examined if EEG signals from traumatic brain injury (TBI) patients can be used for new rehabilitation technologies, such as BCI systems. However, extraction of the high-gamma band related to movement remains challenging due to the presence of surface electromyogram (sEMG) caused by unconscious facial and head movement of patients. In this paper, we proposed a modified independent component analysis (ICA) model for EMG artifact removal in the EEG data from TBI patients with a hemicraniectomy. Here, simulated EMG was generated and added to the raw EEG data as the extra channels for independent components calculation. After running ICA, the independent components (ICs) related to artifacts were identified and rejected automatically through several criteria. EEG data underlying hand movement from one healthy subject and one TBI patient with a hemicraniectomy were conducted to verify the efficacy of this algorithm. Results showed that the proposed algorithm removed sEMG artifacts from the EEG data by up to 86.72% while preserving the associated brain features. In particular, the high-gamma band (80 to 160 Hz) was found to arise principally from the hemicraniectomy area after this technique was applied. Meanwhile, we found that the magnitude of gamma power during movement improved after removal of sEMG artifacts.

Electrocorticographic Activity of the Brain During Micturition

Current therapies for neurogenic bladder do not allow spinal cord injury patients to regain conscious control of urine storage or voiding. Novel neural technologies may provide means to improve or restore the connection between the brain and the bladder; however, the specific brain areas and their underlying neural activities responsible for micturition must be better understood in order to design such technologies. In this retrospective study, we analyzed electrocorticographic (ECoG) data obtained from epilepsy patients who underwent ECoG grid implantation for epilepsy surgery evaluation, in the hopes of determining specific electrophysiological activity associated with micturition. Our results indicate modulation of the delta (δ, 0.1-4 Hz) and low-gamma (\gamma, 25-50 Hz) activity in the peri-Sylvian area and the inferior temporal lobe. These findings suggest involvement of the insular cortex and the uncinate fasciculus in micturition, important structures related to sensation and decision making. To date, this is the first known study utilizing ECoG data to elucidate the electrophysiological activity of the brain associated with bladder control and sensation.

Spatial-Temporal Dynamics of the Sensorimotor Cortex: Sustained and Transient Activity

How the sensorimotor cortex is organized with respect to controlling different features of movement is unclear. One unresolved question concerns the relation between the duration of an action and the duration of the associated neuronal activity change in the sensorimotor cortex. Using subdural electrocorticography electrodes, we investigated in five subjects, whether high frequency band (HFB; 75-135 Hz) power changes have a transient or sustained relation to speech duration, during pronunciation of the Dutch /i/ vowel with different durations. We showed that the neuronal activity patterns recorded from the sensorimotor cortex can be directly related to action duration in some locations, whereas in other locations, during the same action, neuronal activity is transient, with a peak in HFB activity at movement onset and/or offset. This data sheds light on the neural underpinnings of motor actions and we discuss the possible mechanisms underlying these different response types.

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

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.

Decoding Native Cortical Representations for Flexion and Extension at Upper Limb Joints Using Electrocorticography

Brain-machine interface (BMI) researchers have traditionally focused on modeling endpoint reaching tasks to provide the control of neurally driven prosthetic arms. Most previous research has focused on achieving an endpoint control through a Cartesian-coordinate-centered approach. However, a joint-centered approach could potentially be used to intuitively control a wide range of limb movements. We systematically investigated the feasibility of discriminating between flexion and extension of different upper limb joints using electrocorticography(ECoG) recordings from sensorimotor cortex. Four subjects implanted with macro-ECoG (10-mm spacing), high-density ECoG (5-mm spacing), and/or micro-ECoG arrays (0.9-mm spacing and 4 mm × 4 mm coverage), performed randomly cued flexions or extensions of the fingers, wrist, or elbow contralateral to the implanted hemisphere. We trained a linear model to classify six movements using averaged high-gamma power (70-110 Hz) modulations at different latencies with respect to movement onset, and within a time interval restricted to flexion or extension at each joint. Offline decoding models for each subject classified these movements with accuracies of 62%-83%. Our results suggest that the widespread ECoG coverage of sensorimotor cortex could allow a whole limb BMI to sample native cortical representations in order to control flexion and extension at multiple joints.

Characterization of Stimulation Artifact Behavior in Simultaneous Electrocorticography Grid Stimulation and Recording

Bi-directional brain-computer interfaces (BCIs) require simultaneous stimulation and recording to achieve closed-loop operation. It is therefore important that the interface be able to distinguish between neural signals of interest and stimulation artifacts. Current bi-directional BCIs address this problem by temporally multiplexing stimulation and recording. This approach, however, is suboptimal in many BCI applications. Alternative artifact mitigation methods can be devised by investigating the mechanics of artifact propagation. To characterize stimulation artifact behaviors, we collected and analyzed electrocorticography (ECoG) data from eloquent cortex mapping. Ratcheting and phase-locking of stimulation artifacts were observed, as well as dipole-like properties. Artifacts as large as ±1,100 μV appeared as far as 15-37 mm away from the stimulating channel when stimulating at 10 mA. Analysis also showed that the majority of the artifact power was concentrated at the stimulation pulse train frequency (50 Hz) and its super-harmonics (100, 150, 200 Hz). Lower frequencies (0-32 Hz) experienced minimal artifact contamination. These findings could inform the design of future bi-directional ECoG-based BCIs.

Changing Artificial Playback Speed and Real Movement Velocity Do Not Differentially Influence the Excitability of Primary Motor Cortex during Observation of a Repetitive Finger Movement

Action observation studies have investigated whether changing the speed of the observed movement affects the action observation network. There are two types of speed-changing conditions; one involves “changes in actual movement velocity,” and the other is “manipulation of video speed.” Previous studies have investigated the effects of these conditions separately, but to date, no study has directly investigated the differences between the effects of these conditions. In the “movement velocity condition,” increased velocity is associated with increased muscle activity; however, this change of muscle activities is not shown in the “video speed condition.” Therefore, a difference in the results obtained under these conditions could be considered to reflect a difference in muscle activity of actor in the video. The aim of the present study was to investigate the effects of different speed-changing conditions and spontaneous movement tempo (SMT) on the excitability of primary motor cortex (M1) during action observation, as assessed by motor-evoked potentials (MEPs) amplitudes induced by transcranial magnetic stimulation (TMS). A total of 29 healthy subjects observed a video clip of a repetitive index or little finger abduction movement under seven different speed conditions. The video clip in the movement velocity condition showed repetitive finger abduction movements made in time with an auditory metronome, at frequencies of 0.5, 1, 2, and 3 Hz. In the video speed condition, playback of the 1-Hz movement velocity condition video clip was modified to show movement frequencies of 0.5, 2, or 3 Hz (Hz-Fake). TMS was applied at the time of maximal abduction and MEPs were recorded from two right-hand muscles. There were no differences in M1 excitability between the movement velocity and video speed conditions. Moreover, M1 excitability did not vary across the speed conditions for either presentation condition. Our findings suggest that changing playback speed and actual differences in movement velocity do not differentially influence M1 excitability during observation of a simple action task, such as repetitive finger movement, and that it is not affected by SMT. In simple and meaningless observational task, people might not be able to recognize the difference in muscle activity of actor in the video.