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Author: Eric C. Leuthardt
Brain-computer interfaces create alternate communication channels for people with severe motor impairment.
The ability to enable the brain to control an external device with thoughts alone is emerging as a real option for patients with motor disabilities. The goal of this area of study, known as neuroprosthetics, is to create devices, known as brain-computer interfaces (BCIs), that can acquire brain signals and translate them into machine commands that reflect the intentions of the user. In the past 20 years, neuroprosthetics has progressed rapidly from fundamental neuroscientific discovery to initial translational applications.
Seminal discoveries beginning in the 1980s demonstrated that neurons in motor cortex, when taken as a population, can predict the direction and speed of arm movements in monkeys (Georgopoulus et al., 1982, 1986; Moran and Schwartz, 1999b). In subsequent decades, these findings were translated into increasing levels of brain-derived control in monkeys and then to preliminary human clinical trials (Hochberg et al., 2006; Taylor et al., 2002).
In recent years, an emerging understanding of how cortex encodes motor and non-motor intentions, sensory perception, and the role of cortical plasticity in device control have led to new insights into brain function and BCI applications. These new discoveries have greatly increased the potential of neuroprosthetics in terms of control capability and the variety of patient populations that can be helped. This article provides an overview of current BCI modalities and emerging research on the use of motor and non-motor areas for BCI applications and assessments of their potential clinical impact.
Brain-Computer Interfaces: Definition and Essential Features
A BCI is a device that can decode human intent from brain activity alone, thus creating an alternate communication channel for people with severe motor impairment. Explicitly, a BCI does not require the “brain’s normal output pathways of peripheral nerves and muscles” to facilitate interaction with the environment (Wolpaw et al., 2000, 2002). A real-world example would be a quadriplegic subject who can control a cursor on a screen with signals derived from individual neurons recorded in primary motor cortex without the need for overt motor activity. It is important to emphasize that a true BCI creates a completely new output pathway for the brain.
As a new output pathway, the user must have feedback to improve how he or she alters electrophysiological signals. Similar to the development of a new motor skill (e.g., learning to play tennis), there must be continuous alteration of the subject’s neuronal output.
This requires that the output be matched against feedback from the intended actions so that the output (swinging the tennis racket or altering a brain signal) can be tuned to optimize performance to reach the intended goal (hitting the ball over the net or moving a cursor toward a target). Thus, the brain must change its signals to improve performance.
In addition, a BCI may also be able to adapt to the changing milieu of the user’s brain to further optimize functioning. This dual adaptation requires a certain level of training and a learning curve, for both the user and the computer. The better the computer and subject are able to adapt, the shorter the training period to achieve control. Figure 1
There are four essential elements to the practical functioning of a BCI platform (Figure 1). All four elements must work in concert to manifest the user’s intention (Wolpaw et al., 2002):
Signal Acquisition
Signal acquisition is a real-time measurement of the electrophysiological state of the brain. Brain activity is usually measured by voltage changes recorded via electrodes, either invasive (under the skin) or non-invasive (on the surface of the skin). Theoretically, other measurements, such as blood flow detected with magnetic resonance imaging (MRI), altered magnetic fields measured by magnetoencephalography (MEG), and optical signals, might be used for signal acquisition. However, none of these non-electrical signals is currently practical or feasible for clinical application.
The most common types of signals include electro-encephalography (EEG), electrical brain activity recorded from the scalp (Elbert et al., 1980; Farwell and Donchin, 1988; Freeman et al., 2003; Pfurtscheller et al., 1993; Sutter, 1992; Vidal, 1977); electrocorticography (ECoG), electrical brain activity recorded beneath the skull (Leuthardt et al., 2004, 2005; Schalk et al., 2004b); field potentials, electrodes that monitor brain activity from within the parenchyma (Andersen et al., 2004); and “single units,” microelectrodes that monitor the firing of individual neuron action potentials (Georg-opoulus et al., 1986; Kennedy and Bakay, 1998; Laubach et al., 2000; Taylor et al., 2002). Figure 2
Figure 2 shows the relationship between various signal platforms in terms of anatomy and the population sampled. Once acquired, signals are digitized and sent to the BCI system for further interrogation.
Signal Processing
In the signal processing stage of BCI operation, there are two essential functions: feature extraction and signal translation. The first extracts significant identifiable information from the gross signal; the second converts that identifiable information into device commands.
Converting a raw signal into a meaningful one requires an array of analyses, from assessment of frequency power spectra, event-related potentials, and cross-correlation coefficients for analysis of EEG/ECoG signals to the directional cosine tuning of individual neuron action potentials (Levine et al., 2000; Moran and Schwartz, 1999b; Pfurtscheller et al., 2003).
The impetus for all of these analyses is to determine the relationship between an electrophysiologic event and a given cognitive or motor task. For example, after recordings are made from an ECoG signal, the BCI system must recognize that an alteration of the signal has occurred in the electrical rhythm (feature extraction) and then associate the change with a specific cursor movement (translation). As mentioned above, signal processing must be dynamic to adjust to the changing internal signal environment of the user.
Device Output
Overt action (actual device output) is then taken by the BCI. As in the previous example, this can result in moving a cursor on a screen, choosing letters for communication, controlling a robotic arm, driving a wheelchair, or controlling an intrinsic physiologic process, such as moving a limb or controlling bowel and bladder sphincters (Leuthardt et al., 2006b).
Operating Protocol
An important consideration for practical applications of BCI devices is the overall operating protocol. This refers to the manner in which the user controls how the system functions. The how includes, for example, turning the system on or off, controlling the kind of feedback and how fast it is provided, controlling the speed at which the system implements commands, and switching between device outputs.
The way the system functions is critical for BCI functioning in real-world applications. In most current research protocols, the parameters are set by the investigator. In other words, the researcher turns the system on and off, adjusts the speed of interaction, and defines very limited goals and tasks. These are all things the user will eventually have do by him(her)self in an unstructured applied environment.
Current BCI Platforms
To date, three general categories of BCI platforms have been put forward as possible candidates for clinical application: EEG-based systems; single unit systems; and ECoG-based systems, an intermediate modality. The categories are primarily determined by the source from which the controlling brain signal is derived—the scalp, intraparenchymal neurons, or the cortical surface. The current status of each of these platforms is described below in terms of level of control, surgical considerations, and clinical population.
Electroencephalography-Based Systems
EEG-based BCIs derive brain signals from electrical activity recorded from the scalp (Birbaumer et al., 1999; Blankertz et al., 2006; Farwell and Donchin, 1988; Kübler et al., 2005; McFarland et al., 1993, 2008b; Millan et al., 2004; Muller et al., 2008; Pfurtscheller et al., 1993, 2000; Sutter, 1992; Vaughan et al., 2006; Wolpaw et al., 1991; Wolpaw and McFarland, 1994, 2004). EEG-based BCIs are the most common for studies in humans, probably because this recording method is convenient, safe, and inexpensive.
EEG provides relatively poor spatial resolution, because a large brain area must be involved to generate detectable signals (Freeman et al., 2003; Srinivasan et al., 1998). Despite this limitation, signals relevant to BCI research can still be obtained from EEG, including modulations of mu (8–12 Hz) and beta (18–25 Hz) rhythms produced by sensorimotor cortex. These rhythms show non-specific changes (typically decreases in amplitude) related to movements and movement imagery, but they do not provide specific information about the details of movements, such as the position or velocity of hand movements. This may be an important limitation, because signals associated with specific movement parameters are typically used in BCI systems based on action-potential firing rates.
Another limitation of EEG recordings is that the detected amplitudes are very small. This makes them susceptible to artifacts created by sources outside the brain, such as electromyographic (EMG) signals produced by muscle contractions.
Despite these limitations, EEG-based BCIs have been shown to support higher performance than might be expected, including accurate two-dimensional (McFarland et al., 2008b; Wolpaw and McFarland, 2004) and even three-dimensional control of a computer cursor (McFarland et al., 2008a). To date, the large majority of clinical applications of BCI technologies for people with severe motor disabilities have been demonstrated using EEG (Kübler et al., 2005; Nijboer et al., 2008; Vaughan et al., 2006).
Ultimately, however, the intrinsic lack of signal robustness may have important implications for chronic applications of BCI systems in real-world environments. BCI systems based on EEG typically require substantial training (Birbaumer, 2006; Wolpaw and McFarland, 2004) to achieve accurate one- or two-dimensional device control (about 20 and 50 30-minute training sessions, respectively), although some studies have reported shorter training requirements (Blankertz et al., 2006). Nevertheless, noise sensitivity and prolonged training are fundamental limitations in the widespread clinical application of EEG-based BCIs.
In summary, EEG has been shown to support much higher performance than was previously assumed and is currently the only modality that has been shown to actually help people with paralysis. However, because of significant limitations, it is currently not clear to what extent EEG-based BCI performance, in the laboratory and in clinical settings, can be improved.
Single Neuron-Based Systems
From a purely engineering point of view, the optimal method of extracting electrical information from the brain would be to place a series of small recording electrodes directly into the cortical layers (1.5–3 mm) to record signals from individual neurons. In essence, this is what single-unit action-potential BCI systems do. Such systems have been very successful for limited time periods in both monkeys (Carmena et al., 2003; Serruya et al., 2002; Taylor et al., 2002; Velliste et al., 2008) and humans (Hochberg et al., 2006; Kennedy and Bakay, 1998).
To extract single-unit activity, small microelectrodes with ~20 micron diameter tips are inserted into the brain parenchyma where relatively large (e.g., 300 microvolt) extracellular action potentials have been recorded from individual neurons 10–100 microns away. These signals are usually band-passed filtered from 300–10,000 Hz and then passed through a spike discriminator to measure time intervals between spikes.
The firing rates of individual neurons are computed in 10 to 20 millisecond bins and “decoded” to provide high-fidelity control of either a computer cursor or robot endpoint kinematics (Georgopoulus et al., 1986; Moran and Schwartz, 1999a; Wang et al., 2007). Given its high spatial resolution (100 microns) and high temporal resolution (50–100 Hz), this modality arguably provides the highest level of control in BCI applications.
Unfortunately, there are two major problems with single-unit BCIs. First, the electrodes must penetrate into the parenchyma where they cause local neural and vascular damage (Bjornsson et al., 2006). This can initiate a cascade of reactive cell responses, typically characterized by activation and migration of microglia and astrocytes toward the implant site (Bjornsson et al., 2006).
Second, single-unit, action-potential microelectrodes are very vulnerable to encapsulation. The continued presence of electrodes promotes the formation of a sheath composed partly of the reactive astrocytes and microglia (Polikov et al., 2005; Szarowski et al., 2003). This reactive sheath can have numerous deleterious effects, including neural cell death and tissue resistance that electrically isolates the device from the surrounding neural tissue (Biran et al., 2005; Szarowski et al., 2003; Williams et al., 2007).
Research into novel biomaterial coatings and/or local drug delivery systems that may reduce the foreign body response to implanted electrodes is ongoing. So far, however, the results are far from clinical application (Abidian and Martin, 2008; Seymour and Kipke, 2007; Spataro et al., 2005). The development of a long-term BCI system based on single-unit activity may be delayed until these issues have been resolved.
Electrocorticography-Based Systems
Over the past five years, enthusiasm has been mounting for ECoG, a more practical and robust platform for BCI in clinical application. As detailed above, both EEG and single-unit-based systems have serious limitations for large-scale clinical application, either because of prolonged user training and poor signal-to-noise limitations with EEG or because of the inability of single-unit constructs to maintain a consistent signal (Bjornsson et al., 2006; Szarowski et al., 2003; Wolpaw and McFarland, 2004).
ECoG-based systems might be an ideal trade-off for practical implementation (Leuthardt et al., 2005). Compared to EEG, the ECoG signal is substantially more robust. Its magnitude is typically five times larger, its spatial resolution is much greater (0.125 versus 3.0 cm for EEG), and its frequency bandwidth is significantly higher (0–500 Hz versus 0–40 Hz for EEG) (Boulton et al., 1990; Freeman et al., 2003; Srinivasan et al., 1998). The latter quality, access to higher frequency bandwidths, promises particularly useful information for BCI operation (Gaona et al., 2011).
Many studies have demonstrated that different frequency bands carry specific, anatomically distinct information about cortical processing. Lower frequency bands, known as mu (8–12 Hz) and beta (18–26 Hz), which are detectable with EEG, are thought to be produced by thalamocortical circuits. These bands show broad anatomic decreases in amplitude in association with actual or imagined movements (Huggins et al., 1999; Levine et al., 1999; Pfurtscheller et al., 2003; Rohde et al., 2002).
The higher frequencies, also known as gamma activity, are only appreciable with ECoG. Gamma activity, which is thought to be produced by smaller cortical assemblies, shows close correlation with the action-potential firing of tuned cortical neurons in primary motor cortex in monkey models (Heldman et al., 2006). In addition, high-frequency changes have been associated with numerous aspects of speech and motor function in humans (Chao et al., 2010; Crone et al., 1998, 2001a,b; Gaona et al., 2011; Leuthardt et al., 2004; Schalk et al., 2007).
Besides providing more information content, because the ECoG signal is recorded from larger electrodes that do not penetrate the brain, ECoG sensors should have a higher likelihood of long-term clinical durability. This expectation is supported by some pathologic and clinical evidence. For example, in cat, dog, and monkey models, long-term subdural implants showed minimal cortical or leptomeningeal tissue reaction while maintaining prolonged electrophysiologic recording (Bullara et al., 1979; Chao et al., 2010; Loeb et al., 1977; Margalit et al., 2003; Yuen et al., 1987).
So far, ECoG for BCI applications has primarily been studied in motor-intact patients with intractable epilepsy that requires invasive monitoring. Preliminary work in humans using the implantable NeuroPace device for the purpose of long-term subdural electrode monitoring for the identification and abortion of seizures has been shown to be stable (Vossler et al., 2004).
Similar to EEG-based BCI systems, the ECoG approach has primarily focused on changes in sensori-motor rhythms from motor cortex. The major difference has been access to higher frequency gamma rhythms with ECoG, which provide significant advantages in training requirements and multidimensional control.
In 2004, Leuthardt et al. demonstrated the first use of ECoG in closed-loop control in a one-dimensional cursor-control task with minimal training requirements (under 30 minutes). In additional experiments, the same group and others have demonstrated that specific frequency alterations encode very specific information about hand and arm movements (Leuthardt et al., 2004; Pistohl et al., 2008; Sanchez et al., 2008; Schalk et al., 2007). In 2006, Leuthardt et al. (2006a) further demonstrated that ECoG control using a signal from the epidural space was also possible.
Schalk et al. (2008) has shown that ECoG signals can be used for two-dimensional control with performance in the range of typical performance before the testing of invasive single-unit systems. Because ECoG electrode arrays cover broad regions of cortex, several groups have begun to explore alternate cognitive modalities and their cortical physiologies to expand BCI device control. Felton et al. (2007) have shown that, in addition to motor imagery, sensory imagery can also be used for device control. The same group demonstrated that auditory cortex could be trained to acquire simple control of a cursor (Wilson et al., 2006).
Ramsey et al. (2006) showed that higher cognitive functions, such as working memory in the dorsal lateral prefrontal cortex, can also be used for effective device operation. Recently, Leuthardt et al. (2011) demonstrated that phonemic content taken from speech networks could be used for simple device control.
Taken together, these studies show that ECoG signals carry a high level of specific cortical information and that these signals can enable a user to gain control rapidly and effectively. It is worth noting, however, that so far these control paradigms have not been extended to motor-impaired subjects. The effects on cortical signals in the setting of a spinal cord injury or amyotrophic lateral sclerosis (ALS) have not been explicitly tested.
Conclusions
The field of neuroprosthetics is growing rapidly as the cortical physiology that underpins the way a human brain encodes intentions is beginning to be understood. This understanding will have a significant impact in improving function for people with various forms of motor disability. As research moves beyond motor physiology, the field of neuroprosthetics is poised to increase its capabilities and serve the needs of a more diverse clinical population. The evolving understanding of cortical physiology as it relates to motor movements, language function, and plasticity, could all provide higher levels of complexity in brain-derived control.
Given the rapid progression of these technologies over the past decade and the concomitant rapid increase in computer processing speed, signal analysis techniques, and emerging ideas for novel biomaterials, we can be hopeful that in the near future neuro-prosthetic implants will be as common as deep brain stimulators are today. The clinical advent of this technology will usher in a new era of restorative neurosurgery and new human-machine interfaces.
Acknowledgments
The author acknowledges generous support from the following: James S. McDonnell Foundation, Department of Defense (W911NF-07-1-0415 & W911NF-08-1-0216–Leuthardt), and the National Institutes of Health (NINDS NIH R01-EB000856-06), Children’s Discovery Institute.
Financial Disclosure
The author owns stock in the company Neurolutions.
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