Brain-Computer Interface (BCI)

CHAPTER 20

Brain-Computer Interface (BCI) ISAAC JOSH ABECASSIS, MD • ANDREW L. KO, MD

INTRODUCTION Brain-computer (BCI) or brain-machine interfaces, refer to a real-time technology system capable of capturing neural activity (e.g., electrical, chemical, or magnetic) via a recording array and converting this information using mathematical algorithms into a functional output, usually with the governing effects of a behavioral or physiologic conditional signal. At its crux, BCI thereby enables the ability to amplify, modulate, and selectively filter cortical signals in neurologic disease states where the final output signal is diminished or absent, including stroke, spinal cord injury, and neurodegeneration. Accordingly, there is a broad array of various control signals that can be captured and used for BCI. Most efforts have been aimed at improving motor function and thereby overall quality of life, and thus thisdand neuromotor prostheses (NMP)dwill be the predominant focus of this report; however, cognitive, behavioral, and psychologic circuits are also under investigation with clinical trials underway investigating the use of BCI in attention deficit hyperactivity disorder,1 depression, chronic pain, Alzheimer dementia, and autism. Regardless of the substrate, it has been very well demonstrated that region- and pathway-specific corticostriatal plasticity plays an important role in learning both physical2 and abstract3 skill sets and that BCI should be thought of as a bidirectional interaction. Ultimately, the usefulness of BCI therefore hinges on not only the recording, processing and translation of neural signals into functional output but also the chronic, effects of BCI on the brain itself.

BACKGROUND Over the past three decades, there has been an evolution of neural recording technologies and modalities, each with its own unique characteristics. In general, the use of any particular approach to deciphering brain activity carries trade-offs with respect to signal fidelity, spatial and temporal resolution, and information content.

Broadly speaking, electrophysiologic data can be gathered from single cells, or varying sizes of populations of cells, whose activity is reflected in voltage changes measured over time. BCI relies on detectable changes in these signals to affect a functional output. The most commonly used modalities to gather this signal involve penetrating electrodes for single-cell recordings, subdural electrodes for electrocorticography (ECoG), and scalp electroencephalography (EEG). Development of novel signal processing techniques, with concomitant improvements in computing capabilities, has resulted in many available strategies for the extraction, processing, and labeling of electrical brain activity for use in BCI. A full discussion of such techniques is beyond the scope of this chapter. Suffice it to say that there are several canonical signals used for control of a BCI system: 1. Single-unit activity is generally reflected as changes in firing rate of individual neurons and is usually derived from voltage changes measured from microelectrodes at very high frequencies (>400 Hz).4 2. Measures of neuronal population activity such as local field potentials (LFPs) are derived from voltage changes at less than 300 Hz. Changes in voltage related to activity, such as event-related potentials (ERPs), can be seen in the raw signal or by averaging multiple trials. More information can often be derived from varying frequency components within this signal. Traditional frequency bands that have historically been linked to functional activity include the delta (1e4 Hz), theta (4e7 Hz), alpha (7e12 Hz), and beta (13e30 Hz) bands. These signals are usually measured with subdural or scalp electrodes, with signal processing techniques such as the fast Fourier Transform or other time-frequency analyses used to quantify changes in signal components over time.5 Single-unit neuronal activity can be recorded with implantation of approximately 20-mm-diameter electrodes implanted into the cortex, typically with the 143

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goal of recording from layer 5 pyramidal cells in the motor cortex. These microelectrodes are either implanted and fixed at the skull or designed to float freely above the surface of cortex to accommodate motion.6 Kennedy and Bakay first reported successful implantation of cone-shaped electrodes in human motor cortex, coated with “proprietary neurotrophic factors” aimed at promoting neuronal ingrowth, with successful recording of action potentials (APs) in a patient with amyotrophic lateral sclerosis (ALS)7 (Fig. 20.1). The same group went on to describe implantation in human patients with brainstem stroke8 and mitochondrial myopathy9 with recordings of both fast transients (i.e., APs) and LFPs. This latter recording derives from voltage changes measured at electrodes placed sufficiently far from one particular cell, to capture the “synchronized input” of an extracellular cortical area, with a low pass filter to remove high-frequency fluctuations and individual spikes. Subsequent efforts were aimed at harnessing the tenets of the “population vector” method10dwhereby individual cells are represented as vectors with weighted contributions along the axis of a preferred directiondfor precise three-dimensional motor control. Although efforts to use neuronal activity as a control signal for BCI have been successful in the acute setting, single-unit recording microelectrodes are prone to highimpedance, gliotic sheath formation in chronic models owing to micromotion,11 resulting in degradation of this control signal. As a result, investigators have looked at alternative markers for brain activity.

Local field potential recordings have emerged as a major tool for driving long-term, chronically implanted BCI. Voltage changes occurring at lower frequencies (70e300 Hz) than single-unit APsda range termed the “high g band” (HGB)dhave been demonstrated to correlate to single-unit activity recorded from penetrating electrodes.6,12 Such signals can be obtained using subdural ECoG,6,13 which offers a less invasive (nonpenetrating) form of recording that is already widely used in surgical epilepsy practices. Epidural ECoG has been reported in nonhuman primates14 and subgaleal ECoG in pediatric subjects,15 both of which associated with an inherent decrease in risk of meningitis and cortical irritation and with similar cortical recording qualities. Moreover, the cortical signals obtained via ECoG have been demonstrated to be stable over time, with reliable indicators of sensorimotor activity unchanged over many months.16 Rather than detecting single-cell firing rates as with single-unit BCI systems, ECoG-based BCI, by and large, relies on changes in firing rates of populations of cells being recorded or shifts toward more synchronous activity, which can be easily and reliably detected.17 For example, changes in HGB power have been shown to correlate with spatially and temporally specific local cortical activity involved in the sensorimotor system,5,18 as well as activity in other cognitive domains. ECoG electrodes, like microelectrodes, require surgical implantation and are thus invasive. Spatial coverage with these electrodes is also necessarily limited. EEG, on the other hand, does not necessarily involve surgery. Electrodes are affixed to the scalp and record

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Kennedy PR, Bakay RA. Restoration of neural output from a paralyzed patient by a direct brain connection. Neuroreport. 1998;9(8):1707e1711, with permission.)

CHAPTER 20 signals reflecting the activity of large volumes of brain. This approach allows, in principle, recording voltage changes across the entire supratentorial cortex. Electrical activity has historically been examined in certain lowfrequency bands, which are thought to have functional significance: delta (1e4 Hz), theta (4e7 Hz), alpha (7e12 Hz), and beta (13e30 Hz) bands. Importantly, scalp EEG recordings fail to capture HGB activity because of the high resistance and capacitance in the scalp and soft tissues,19 which filter the higher frequencies generated by smaller neuronal populations and compromising the degree of spatial resolution.20,21 Moreover, artifacts from muscle activity can often make signal processing challenging. The noninvasive nature of EEG, however, remains a benefit with respect to study recruitment, risks to experimental subjects, and the ability to repeat experiments over time. In short, each of these approaches has its advantages and disadvantages. Microelectrode-based techniques have high temporal and spatial specificity, and individual cell activity can provide a great deal of information at the cost of being potentially damaging to cortex, with decreasing signal fidelity over time. Widespread spatial coverage of disparate cortical regions may also be impractical. ECoG can cover greater cortical regions, but with loss of spatial resolution, but may be more durable and less invasive. EEG offers wider coverage still and does not require surgical implantation; however, this modality does not offer the spatial or frequency resolution of more invasive techniques. Overall, two distinct strategies have emerged for how the BCI process implements its functional output. The first aims to complete substitution of lost motor function via “bypassing” the corticospinal tract (assistive BCI, including both noninvasive and invasive technologies); the second approach uses BCI to identify and augment intrinsic neuroplasticity using biofeedback (rehabilitative or restorative BCI), which can sometimes involve brain-computer-brain interfaces (BCBIs).22

ASSISTIVE BRAIN-COMPUTER INTERFACE Noninvasive: Beta and Mu Waves, Beta-Band Desynchronization, Slow Cortical Potentials, and Evoked Response Potentials Assistive BCI devices rely on an elegant neuroprosthetic design that can seamlessly incorporate into various activities of daily living. Noninvasive modalities for capturing neural recordings largely depend on capturing various segments or frequencies from EEG recordings. At rest, idle cortical activity is conveyed via spontaneous EEG activity with spatiotemporal patterns of “event-related synchronization”. Motor, sensory, or

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cognitive-related cortical activity by both imagery and/ or execution creates a local power attenuation in EEG signal termed “event-related desynchronization” (ERD).23 Sensorimotor EEG activity consists of mu waves at rest (8e13 Hz, in the same frequency range as alpha waves; however, they will not be eliminated with eye opening like posterior dominant occipital alpha waves will) and beta (beta-2 or middle level beta, 15e25 Hz) frequency bands.24 Each sensorimotor area and supplementary motor area, thus, has a unique intrinsic beta rhythm that undergoes desynchronization with activation, whereby mu activity is suppressed with intended or executed action.25 Moreover, the amplitude and specific characteristics of beta-band ERD vary during action planning based on the category of action sequences and time course of the actions that are intended.26 McFarland et al. reported effective movement of a computer cursor in one or two dimensions using mu- and beta-rhythm control.27,28 Birbaumer et al. reported the use of BCI in two patients with advanced ALS and a clinically “locked-in syndrome” (similar to the efforts of Kennedy and Bakay), with total loss of motor function/expression and artificial ventilator and feeding support.29 Slow cortical potentials (SCPs, also called infra-low waves, < 0.5 Hz) were recorded from electroencephalogram (EEG) as well as eye movements, and the subjects were trained to control SCPs based on replicating the visual (upper vs. lower half of the screen) and audiometric (specific tones) features of a signal on a monitor in front of them. SCPs are an event-related (e.g., based on response to a visual or auditory cue), general electrical activity amplitude metric that occurs over 300 ms to multiple seconds (relatively slow for EEG). Increased SCPs negativity reflects greater cell depolarization, a lower threshold for excitement, and hence it is thought to reflect increased neuronal activity. Initial training in this study advanced to the ability to copy letters and words and ultimately into free spelling. Other efforts capturing “gaze patterns”30 enabled some function for cursor manipulation as well, however, with the obvious limitation of obstructing natural function (i.e., obstructing gaze). Nonetheless, the fastest speeds of spelling and communication have been demonstrated with the addition of visual evoked potentials (VEPs)31 and steady-state VEPs,32 ranging from 20 to 60 characters per minute, respectively. Event-related potentials (ERPs), or the P300 wave as recorded on EEG, are positive deflections in voltage associated with typically audio or visual stimuli. There are two subcomponents including a “P3a” peak (with maximum amplitude over the frontal region and associated with stimulus-driven attention during a task) and a “P3b”

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peak (with maximum amplitude over the parietal region, however, to be related to improbable events and memory processing).33 P300-based BCI has been demonstrated with auditory or visual stimuli.34,35Sophisticated combinations of technologies have incorporated audiometric feedback (via auditory evoked potentials) into an spinal cord injury (SCI) prosthetic; however, the concept remains to be validated in a disease state.36 Finally, some groups have attempted to bridge various EEG input BCI techniques to a virtual reality interface.37

InvasivedElectrocorticography and Gamma Band Activity ECoG was first used to capture high gamma range frequencies (40e180 Hz) from sensorimotor and speech

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CHAPTER 20 cortical stimulation over somatosensory cortex in a spatially and temporally congruent manner can induce a feeling of ownership of a “rubber hand,” whereas asynchronous stimulation does not .41 Such experiments show that the brain is capable of integrating visual input with direct somatosensory stimulation to create multisensory perceptions, a bidirectional interface that has the potential to bypass peripheral sensory input, and may be of particularly importance in patients who lack such input owing to spinal cord or other CNS lesions.

InvasivedSingle-Unit Recording Translation from a computer cursor to actual motor function was first modulated in nonhuman primate studies. Moritz et al. captured cortical recordings of the hand and wrist motor areas in monkeys and converted these neural signals into proportional stimuli that were delivered to the actual muscles of the arm and handdtermed “functional electrical stimulation”d after selectively implanting catheters into the radial, ulnar, and median nerves and injecting anesthetic for temporary paralysis.42 Monkeys were rewarded for displaying smooth control of the limb and accordingly were able to train single neuron discharge patterns from the wrist and hand motor cortex, a concept termed “operant conditioning,” which had been reported much earlier by Fetz and Finnochio.43 These efforts were expanded by Pohlmeyer et al.44 to include more expansive cortical recording and four rather than two forearm muscles, thus enabling more elaborate upper extremity function. Ultimately, other nonhuman primate studies45 paved the way for a human-compatible design to record from groups of cells. With critical financial support by the Defense Advanced Research Projects Agency (DARPA), Hochberg et al.46 reported the first use of the BrainGate (Cyberkinetics, Inc.) NMP, a 10  10 96-channel array of microelectrodes 1 mm in depth with a 4  4 mm base, implanted into the hand motor cortex of a quadriplegic spinal cord injury patient, fixed at the skull, with a bundle of cables that are connected externally to a computer system to filter the signal into a cursor that the patient could directly manipulate and direct. AP spikes were recorded from both single and groups of motor neurons, with simultaneous recording of the LFPs (Fig. 20.3). Three populations of neurons emerged including (1) a nonspecific group, (2) a group specific to “imagined” motor movement, and (3) a group correlated to actual (proximal shoulder) muscle activity. This allowed the group to construct a novel linear filter to decode neural activity and highlight “intended action” to be used as a filter. A few years later, the same group reported on additional testing in two patients with

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ALS and brainstem stroke, whereby they validated a method for decoding kinematics (velocity) with a Kalman filter (a recursive, Bayesian inference algorithm) as an improved conversion tool than the previously described linear filter for position of the cursor (i.e., participants achieved better cursor control at a faster rate in a closed-loop system)47 (Fig. 20.4). Ultimately, the group reported quite sophisticated motor function in an upper-extremity NMP, including threedimensional reach and grasp tasks and drinking coffee from a bottle (through a straw).48 More recently, Collinger et al. implanted two arrays in a tetraplegic patient with spinocerebellar degeneration and successfully reported seven-dimensional control (including threedimensional orientation, three-dimensional translation, and one-dimensional grasping) using a novel prosthetic device after 13 weeks of training.49

REHABILITATIVE BRAIN-COMPUTER INTERFACE Closed-Loop Systems: Brain-ComputerBrain Interfaces The governing principles underlying rehabilitative BCI are similar to those underlying basic neurorehabilitation, in which there is a dependence on coordinating novel neuronal ensembles pathways, with adequate rewards to engender a Hebbian plasticity or learning process.22,50 In other words, rather than using a prosthetic limb to execute endogenous neural activity (albeit with a training process and mathematical algorithm for translation), neural activity is recorded and streamlined through a new conduit within the body so as to harness a “brain-computer-brain” or “brain-spine” circuit. Canadian psychologist Donald Hebb is credited with first developing the underlying concepts that eventually precipitated the field of “Hebbian Plasticity,” whereby repetitive assistance with electrical firing of one neuron by another leads to the creation of new interneuron connections. More recent advances in BCI have yielded neuroprosthetic designs that capture a signal from one population of neurons and use this as an input to regulate stimulation of a distinctly separate population of neurons. For example, Guggenmos et al.51 in a rodent TBI model recruited an adjacent population of neurons through “paired-associated stimulation” (i.e., cortical stimulation of a nearby population of cells during detected premotor action intention) and augmented motor recovery response. Other efforts have focused on a closed-loop circuit involving detection of cortical signals, bypassing an injury, and stimulation of the spinal cord. Jackson et al. first reported the use of a novel battery-powered,

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neuronal units (33), a single unit (34), a low-amplitude signal (22), and triggered noise (95). (B) Local field potential recordings from one electrode at three positions (bottom panel) before and after a subject is instructed to move a computer cursor into a part of the screen. The top panel is a pseudocolor power spectral density plot constructed by performing a time-frequency analysis. (From Hochberg LR, Serruya MD, Friehs GM, et al. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature. 2006; 442(7099):164e171, with permission.)

CHAPTER 20 S3–40, 80 paths, N=179

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FIG. 20.4 Neural cursor movement with different filtering techniques. Yellow boxes approximate target goals. N denotes number of recorded units. Each path (gray, blue, black, and green) comprised 80 neural cursor movements. (A) Position-based linear filter in three recording sessions, raw data on the top and filtered data on the bottom. (B) Velocity-based Kalman filter in three recording sessions, raw data on the top and filtered data on the bottom. (From Kim SP, Simeral JD, Hochberg LR, Donoghue JP, Black MJ. Neural control of computer cursor velocity by decoding motor cortical spiking activity in humans with tetraplegia. J Neural Eng. 2008;5(4): 455e476, with permission.)

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“Neurochip” BCI attached to a macaque monkey skull.52 The device comprises two separate Programmable System-on-Chips (PSoCs) (Cypress Semiconductor Corporation) in parallel, each an 8-bit microprocessor core with the abilities to detect, record, and process APs. One is dedicated to recording cortical data from primary motor cortex (where there are 12 microwires, each 50 mm in diameter) and transmitting the collective data via infrared for external storage/analysis, and the other is responsible for sampling EMG activity from forearm muscles. The two PSoCs communicate,

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synchronize, rectify (for EMG), filter, and amplify the input signals, with an output electrode capable of microstimulation placed into the cervical spinal cord. Since its inception, intraspinal microstimulation (ISMS) has shown promise in rodent models of cervical contusion SCI.53,54 Similarly, other groups have validated ISMS in the lumbar spine for locomotion in a rodent model.55 Closed-loop neuromodulation systems of cortical recording and ISMS have been shown to be efficacious in SCI in both rodent56 and nonhuman primate models (Fig. 20.5).57

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FIG. 20.5 Schematic for brain-computer-spine interface, whereby neural signals are recorded in nonhuman primate motor cortex, modulated, and transmitted to an epidural site for electrical stimulation. This is “closedloop” type system. (From Capogrosso M, Milekovic T, Borton D, et al. A brain-spine interface alleviating gait deficits after spinal cord injury in primates. Nature. 2016;539(7628):284e288, with permission.)

CHAPTER 20

CONCLUSION In summary, BCI techniques include both noninvasive (EEG) and invasive modalities (ECoG and microelectrode placement) for recording neural signals, within either an assistive (external NMP) or rehabilitative (BCBI, native reanimation) design. Critical to effective performance is an intelligent interpretation, translation, and modulation of raw neural signal into meaningful output. The modalities used to gather this signal often trade spatial and functional resolution for invasiveness and durability of signal; microelectrode arrays have great potential for information bandwidth and dimensionality but are invasive and subject to signal degradation over time. Increasing attention is being paid to bidirectional BCI, where output is directed not only toward an external functional effector but also back to the brain itself, through efforts to provide sensory or multisensory feedback. Although most current efforts have been geared toward sensorimotor restoration, future work aims to build on specific cognitive domains as well.

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