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.

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.

Cortical sites critical to language function act as connectors between language subnetworks.

Historically, eloquent functions have been viewed as localized to focal areas of human cerebral cortex, while more recent studies suggest they are encoded by distributed networks. We examined the network properties of cortical sites defined by stimulation to be critical for speech and language, using electrocorticography from sixteen participants during word-reading. We discovered distinct network signatures for sites where stimulation caused speech arrest and language errors. Both demonstrated lower local and global connectivity, whereas sites causing language errors exhibited higher inter-community connectivity, identifying them as connectors between modules in the language network. We used machine learning to classify these site types with reasonably high accuracy, even across participants, suggesting that a site's pattern of connections within the task-activated language network helps determine its importance to function. These findings help to bridge the gap in our understanding of how focal cortical stimulation interacts with complex brain networks to elicit language deficits.

Reducing power requirements for high-accuracy decoding in iBCIs.

Objective.Current intracortical brain-computer interfaces (iBCIs) rely predominantly on threshold crossings ('spikes') for decoding neural activity into a control signal for an external device. Spiking data can yield high accuracy online control during complex behaviors; however, its dependence on high-sampling-rate data collection can pose challenges. An alternative signal for iBCI decoding is the local field potential (LFP), a continuous-valued signal that can be acquired simultaneously with spiking activity. However, LFPs are seldom used alone for online iBCI control as their decoding performance has yet to achieve parity with spikes.Approach.Here, we present a strategy to improve the performance of LFP-based decoders by first training a neural dynamics model to use LFPs to reconstruct the firing rates underlying spiking data, and then decoding from the estimated rates. We test these models on previously-collected macaque data during center-out and random-target reaching tasks as well as data collected from a human iBCI participant during attempted speech.Main results.In all cases, training models from LFPs enables firing rate reconstruction with accuracy comparable to spiking-based dynamics models. In addition, LFP-based dynamics models enable decoding performance exceeding that of LFPs alone and approaching that of spiking-based models. In all applications except speech, LFP-based dynamics models also facilitate decoding accuracy exceeding that of direct decoding from spikes.Significance.Because LFP-based dynamics models operate on lower bandwidth and with lower sampling rate than spiking models, our findings indicate that iBCI devices can be designed to operate with lower power requirements than devices dependent on recorded spiking activity, without sacrificing high-accuracy decoding.

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.

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

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 ± 0.3 fractional variance accounted for (FVAF)] and force (0.7 ± 0.2 FVAF) with high accuracy, yet found different spatial representations. In addition, we used a state-of-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%). 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).

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.

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.

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 neural activity to predict rat locomotion using intracortical and epidural arrays.

OBJECTIVE: Recovery of voluntary gait after spinal cord injury (SCI) requires the restoration of effective motor cortical commands, either by means of a mechanical connection to the limbs, or by restored functional connections to muscles. The latter approach might use functional electrical stimulation (FES), driven by cortical activity, to restore voluntary movements. Moreover, there is evidence that this peripheral stimulation, synchronized with patients' voluntary effort, can strengthen descending projections and recovery. As a step towards establishing such a cortically-controlled FES system for restoring function after SCI, we evaluate here the type and quantity of neural information needed to drive such a brain machine interface (BMI) in rats. We compared the accuracy of the predictions of hindlimb electromyograms (EMG) and kinematics using neural data from an intracortical array and a less-invasive epidural array. APPROACH: Seven rats were trained to walk on a treadmill with a stable pattern. One group of rats (n = 4) was implanted with intracortical arrays spanning the hindlimb sensorimotor cortex and EMG electrodes in the contralateral hindlimb. Another group (n = 3) was implanted with epidural arrays implanted on the dura overlying hindlimb sensorimotor cortex. EMG, kinematics and neural data were simultaneously recorded during locomotion. EMGs and kinematics were decoded using linear and nonlinear methods from multiunit activity and field potentials. MAIN RESULTS: Predictions of both kinematics and EMGs were effective when using either multiunit spiking or local field potentials (LFPs) recorded from intracortical arrays. Surprisingly, the signals from epidural arrays were essentially uninformative. Results from somatosensory evoked potentials (SSEPs) confirmed that these arrays recorded neural activity, corroborating our finding that this type of array is unlikely to provide useful information to guide an FES-BMI for rat walking. SIGNIFICANCE: We believe that the accuracy of our decoders in predicting EMGs from multiunit spiking activity is sufficient to drive an FES-BMI. Our future goal is to use this rat model to evaluate the potential for cortically-controlled FES to be used to restore locomotion after SCI, as well as its further potential as a rehabilitative technology for improving general motor function.