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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.
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 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 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.
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.
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).
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.
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.
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.
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.
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.
The author owns stock in the company Neurolutions.
Abidian, M.R., and D.C. Martin. 2008. Experimental and theoretical characterization of implantable neural microelectrodes modified with conducting polymer nanotubes. Biomaterials 29(9): 1273–1283.
Andersen, R.A., J.W. Burdick, S. Musallam, B. Pesaran, and J.G. Cham. 2004. Cognitive neural prosthetics. Trends in Cognitive Science 8(11): 486–493.
Biran, R., D.C. Martin, and P.A. Tresco. 2005. Neuronal cell loss accompanies the brain tissue response to chronically implanted silicon microelectrode arrays. Experimental Neurology 195(1): 115–126.
Birbaumer, N. 2006. Brain-computer-interface research: coming of age. Clinical Neurophysiology 117(3): 479–483.
Birbaumer, N., N. Ghanayim, T. Hinterberger, I. Iversen, B. Kotchoubey, A. Kübler, J. Perelmouter, E. Taub, and H. Flor. 1999. A spelling device for the paralysed. Nature 398(6725): 297–298.
Bjornsson, C.S., S.J. Oh, Y.A Al-Kofahi, Y.J. Lim, K.L. Smith, J.N. Turner, S. De, B. Roysam, W. Shain, and S.J. Kim. 2006. Effects of insertion conditions on tissue strain and vascular damage during neuroprosthetic device insertion. Journal of Neural Engineering 3(3): 196–207.
Blankertz, B., G. Dornhege, M. Krauledat, K.R. Muller, V. Kunzmann, F. Losch, and G. Curio. 2006. The Berlin brain-computer interface: EEG-based communication without subject training. IEEE Transactions on Neural Systems and Rehabilitation Engineering 14(2): 147–152.
Boulton, A.A., G.B. Baker, and C.H. Vanderwolf. 1990. Neurophysiological Techniques, II: Applications to Neural Systems. New York: Humana Press.
Bullara, L.A., W.F. Agnew, T.G. Yuen, S. Jacques, and R.H. Pudenz. 1979. Evaluation of electrode array material for neural prostheses. Neurosurgery 5(6): 681–686.
Carmena, J., M. Lebedev, R. Crist, J. O’Doherty, D. Santucci, D. Dimitrov, S. Patil, C. Henriquez, and M. Nicolelis. 2003. Learning to control a brain-machine interface for reaching and grasping by primates. PLoS Biology 1(2): 193–208.
Chao, Z.C., Y. Nagasaka, and N. Fujii. 2010. Long-term asynchronous decoding of arm motion using electrocorticographic signals in monkeys. Frontiers in Neuroengineering 3: 3.
Crone, N.E., D.L. Miglioretti, B. Gordon, R.P. and Lesser. 1998. Functional mapping of human sensorimotor cortex with electrocorticographic spectral analysis. II. Event-related synchronization in the gamma band. Brain 121(Pt 12): 2301–2315.
Crone, N.E., L. Hao, J. Hart Jr., D. Boatman, R.P. Lesser, R. Irizarry, and B. Gordon. 2001a. Electrocorticographic gamma activity during word production in spoken and sign language. Neurology 57(11): 2045–2053.
Crone, N.E., D. Boatman, B. Gordon, L. and Hao. 2001b. Induced electrocorticographic gamma activity during auditory perception. Brazier Award-winning article. Clinical Neurophysiology 112(4): 565–582.
Elbert, T., B. Rockstroh, W. Lutzenberger, and N. Birbaumer. 1980. Biofeedback of slow cortical potentials. Electroencephalography and Clinical Neurophysiology 48(3): 293–301.
Farwell, L.A., and E. Donchin. 1988. Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalography and Clinical Neurophysiology 70(6): 510–523.
Felton, E.A., J.A. Wilson, J.C. Williams, and P.C. Garell. 2007. Electrocorticographically controlled brain-computer interfaces using motor and sensory imagery in patients with temporary subdural electrode implants. Report of four cases. Journal of Neurosurgery 106(3): 495–500.
Freeman, W.J., M.D. Holmes, B.C. Burke, and S. Vanhatalo. 2003. Spatial spectra of scalp EEG and EMG from awake humans. Clinical Neurophysiology 114(6): 1053–1068.
Gaona, C., M. Sharma, Z. Freudenburg, J. Breshears, D. Bundy, J. Roland, D. Barbour, G. Schalk, and E. Leuthardt. 2011. Nonuniform high-gamma (60–500 Hz) power changes dissociate cognitive task and anatomy in human cortex. Journal of Neuroscience 31(6): 2091–2100.
Georgopoulos, A.P., J.F. Kalaska, R. Caminity, and J.T. Massey. 1982. On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex. Journal of Neuroscience 2(11): 1527–1537.
Georgopoulos, A.P., A.B. Schwartz, and R.E. Kettner. 1986. Neuronal population coding of movement direction. Science 233(4771): 1416–1419.
Heldman, D.A., W. Wang, S.S. Chan, D.W. and Moran. 2006. Local field potential spectral tuning in motor cortex during reaching. IEEE Transactions on Neural System Rehabilitation Engineering 14(2): 180–183.
Hochberg, L.R., M.D. Serruya, G.M. Friehs, J.A. Mukand, M. Saleh, A.H. Caplan, A. Branner, D. Chen, R.D. Penn, and J.P. Donoghue. 2006. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 442(7099): 164–171.
Huggins, J.E., S.P. Levine, S.L. BeMent, R.K. Kushwaha, L.A. Schuh, E.A. Passaro, M.M. Rohde, D.A. Ross, K.V. Elisevich, and B.J. Smith. 1999. Detection of event-related potentials for development of a direct brain interface. Journal of Clinical Neurophysiology 16(5): 448–455.
Jarosiewicz, B., S.M. Chase, G.W. Fraser, M. Velliste, R.E. Kass, and A.B. Schwartz. 2008. Functional network reorganization during learning in a brain-computer interface paradigm. Proceedings of the National Academy of Sciences of the U.S.A. 105(49): 19486–19491.
Kennedy, P.R., and R.A. Bakay. 1998. Restoration of neural output from a paralyzed patient by a direct brain connection. Neuroreport 9(8): 1707–1711.
Kübler, A., F. Nijboer, J. Mellinger, T.M. Vaughan, H. Pawelzik, G. Schalk, D.J. McFarland, N. Birbaumer, and J.R. Wolpaw. 2005. Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface. Neurology 64(10): 1775–1777.
Laubach, M., J. Wessberg, and M.A. Nicolelis. 2000. Cortical ensemble activity increasingly predicts behaviour outcomes during learning of a motor task. Nature 405(6786): 567–571.
Leuthardt, E.C., G. Schalk, J.R. Wolpaw, J.G. Ojemann, and D.W. Moran. 2004. A brain-computer interface using electrocorticographic signals in humans. Journal of Neural Engineering 1(2): 63–71.
Leuthardt, E.C., K.J. Miller, G. Schalk, R.N. Rao, and J.G. Ojemann. 2005. Electrocorticography-Based Brain Computer Interface - The Seattle Experience. IEEE - Neural Systems and Rehabilitation Engineering 2005.
Leuthardt, E.C., K.J. Miller, G. Schalk, R.P. Rao, and J.G. Ojemann. 2006a. Electrocorticography-based brain computer interface—the Seattle experience. IEEE Transactions on Neural Systems and Rehabilitation Engineering 14(2): 194–198.
Leuthardt, E.C., G. Schalk, D. Moran, and J.G. Ojemann. 2006b. The emerging world of motor neuroprosthetics: a neurosurgical perspective. Neurosurgery 59(1): 1–14; discussion 1–14.
Leuthardt, E.C., G. Schalk, J. Roland, A. Rouse, and D.W. Moran. 2009. Evolution of brain-computer interfaces: going beyond classic motor physiology. Neurosurgical Focus 27(1): E4.
Leuthardt, E.C., C. Gaona, M. Sharma, N. Szrama, J. Roland, Z. Freudenberg, J. Solis, J. Breshears, and G. Schalk. 2011. Using the electrocorticographic speech network to control a brain-computer interface in humans. Journal of Neural Engineering 8(3): 036004.
Levine, S.P., J.E. Huggins, S.L. BeMent, R.K. Kushwaha, L.A. Schuh, E.A. Passaro, M.M. Rohde, and D.A. Ross. 1999. Identification of electrocorticogram patterns as the basis for a direct brain interface. Journal of Clinical Neurophysiology 16(5): 439–447.
Levine, S.P., J.E. Huggins, S.L. BeMent, R.K. Kushwaha, L.A. Schuh, M.M. Rohde, E.A. Passaro, D.A. Ross, K.V. Elisevich, and B.J. Smith. 2000. A direct brain interface based on event-related potentials. IEEE Transactions on Rehabilitation Engineering 8(2): 180–185.
Loeb, G.E., A.E. Walker, S. Uematsu, and B.W. Konigsmark. 1977. Histological reaction to various conductive and dielectric films chronically implanted in the subdural space. Journal of Biomedical Materials Research 11(2): 195–210.
Margalit, E., J.D. Weiland, R.E. Clatterbuck, G.Y. Fujii, M. Maia, M. Tameesh, G. Torres, S.A. D’Anna, S. Desai, D.V. Piyathaisere, A. Olivi, E. de Juan Jr., and M.S. Humayun. 2003. Visual and electrical evoked response recorded from subdural electrodes implanted above the visual cortex in normal dogs under two methods of anesthesia. Journal of Neuroscience Methods 123(2): 129–137.
McFarland, D.J., G.W., Neat, and J.R. Wolpaw. 1993. An EEG-based method for graded cursor control. Psychobiology 21: 77–81.
McFarland, D.J., W. Sarnacki, and J.R. Wolpaw. 2008a. Electroencephalographic (EEG) control of three-dimensional movement. Society for Neuroscience Annual Meeting 2008, Washington, D.C.
McFarland, D., D. Krusienski, W. Sarnacki, and J. Wolpaw. 2008b. Emulation of computer mouse control with a noninvasive brain–computer interface. Journal of Neural Engineering 5(2): 101–110.
Millan, J.R., F. Renkens, J. Mourino, and W. Gerstner. 2004. Noninvasive brain-actuated control of a mobile robot by human EEG. IEEE Transactions on Biomedical Engineering 51(6): 1026–1033.
Moran, D.W., and A.B. Schwartz. 1999a. Motor cortical activity during drawing movements: population representation during spiral tracing. Journal of Neurophysiology 82(5): 2693–2704.
Moran, D.W., and A.B. Schwartz. 1999b. Motor cortical representation of speed and direction during reaching. Journal of Neurophysiology 82(5): 2676–2692.
Muller, K.R., M. Tangermann, G. Dornhege, M. Krauledat, G. Curio, and B. Blankertz. 2008. Machine learning for real-time single-trial EEG-analysis: from brain-computer interfacing to mental state monitoring. Journal of Neuroscience Methods 167(1): 82–90.
Nijboer, F., E.W. Sellers, J. Mellinger, M.A. Jordan, T. Matuz, A. Furdea, S. Halder, U. Mochty, D.J. Krusienski, T.M. Vaughan, J.R. Wolpaw, N. Birbaumer, and A. Kubler. 2008. A P300-based brain-computer interface for people with amyotrophic lateral sclerosis. Clinical Neurophysiology 119(8): 1909–1916.
Pfurtscheller, G., D. Flotzinger, and J. Kalcher. 1993. Brain-computer interface: a new communication device for handicapped persons. Journal of Microcomputer Applications 16(3): 293–299.
Pfurtscheller, G., C. Guger, G. Mueller, G. Krausz, and C. Neuper. 2000. Brain oscillations control hand orthosis in a tetraplegic. Neuroscience Letters 292(3): 211–214.
Pfurtscheller, G., B. Graimann, J.E. Huggins, S.P. Levine, and L.A. Schuh. 2003. Spatiotemporal patterns of beta desynchronization and gamma synchronization in corticographic data during self-paced movement. Clinical Neurophysiology 114(7): 1226–1236.
Pistohl, T., T. Ball, A. Schulze-Bonhage, A. Aertsen, and C. Mehring. 2008. Prediction of arm movement trajectories from ECoG-recordings in humans. Journal of Neuroscience Methods 167(1): 105–114.
Polikov, V.S., P.A. Tresco, and W.M. Reichert. 2005. Response of brain tissue to chronically implanted neural electrodes. Journal of Neuroscience Methods 148(1): 1–18.
Ramsey, N.F., M.P. van de Heuvel, K.H. Kho, and F.S. Leijten. 2006. Towards human BCI applications based on cognitive brain systems: an investigation of neural signals recorded from the dorsolateral prefrontal cortex. IEEE Transactions on Neural System Rehabilitation Engineering 14(2): 214–217.
Rohde, M.M., S.L. BeMent, J.E. Huggins, S.P. Levine, R.K. Kushwaha, and L.A. Schuh. 2002. Quality estimation of subdurally recorded, event-related potentials based on signal-to-noise ratio. IEEE Transactions on Biomedical Engineering 49(1): 31–40.
Rouse, A.G., and D.W. Moran. 2009. Neural adaptation of epidural electrocorticographic (EECoG) signals during closed-loop brain computer interface (BCI) tasks. Pp. 5514–5517in Proceedings of the Annual International Conference IEEE Engineering in Medicine and Biology Society 2009. New York: IEEE.
Sanchez, J.C., A. Gunduz, P.R. Carney, and J.C. Principe. 2008. Extraction and localization of mesoscopic motor control signals for human ECoG neuroprosthetics. Journal of Neuroscience Methods 167(1): 63–81.
Schalk, G., D.J. McFarland, T. Hinterberger, N. Birbaumer, and J.R. Wolpaw. 2004a. BCI2000: a general-purpose brain-computer interface (BCI) system. IEEE Transactions on Biomedical Engineering 51(6): 1034–1043.
Schalk, G., E.C. Leuthardt, D. Moran, J. Ojemann, and J.R. Wolpaw. 2004b. Two-dimensional cursor control using electrocorticographic signals in humans. Presented at the Society for Neuroscience, San Diego, Calif., October 23, 2004.
Schalk, G., J. Kubanek, K.J. Miller, N.R. Anderson, E.C. Leuthardt, J.G. Ojemann, D. Limbrick, D. Moran, L.A. Gerhardt, and J.R. Wolpaw. 2007. Decoding two-dimensional movement trajectories using electrocorticographic signals in humans. Journal of Neural Engineering 4(3): 264–275.
Schalk, G., K.J. Miller, N.R. Anderson, J.A. Wilson, M.D. Smyth, J.G. Ojemann, D.W. Moran, J.R. Wolpaw, and E.C. Leuthardt. 2008. Two-dimensional movement control using electrocorticographic signals in humans. Journal of Neural Engineering 5(1): 75–84.
Serruya, M.D., N.G. Hatsopoulos, L. Paninski, M.R. Fellows, and J.P. Donoghue. 2002. Instant neural control of a movement signal. Nature 416(6877): 141–142.
Seymour, J.P., and D.R. Kipke. 2007. Neural probe design for reduced tissue encapsulation in CNS. Biomaterials 28(25): 3594–3607.
Spataro, L., J. Dilgen, S. Retterer, A.J. Spence, M. Isaacson, J.N. Turner, and W. Shain. 2005. Dexamethasone treatment reduces astroglia responses to inserted neuroprosthetic devices in rat neocortex. Experimental Neurology 194(2): 289–300.
Srinivasan, R., P.L. Nunez, and R.B. Silberstein. 1998. Spatial filtering and neocortical dynamics: estimates of EEG coherence. IEEE Transactions on Biomedical Engineering 45(7): 814–826.
Sutter, E.E. 1992. The brain response interface: communication through visually-induced electrical brain responses. Journal of Microcomputer Applications 15(1): 31–45.
Szarowski, D.H., M.D. Andersen, S. Retterer, A.J. Spence, M. Isaacson, H.G. Craighead, J.N. Turner, and W. Shain. 2003. Brain responses to micro-machined silicon devices. Brain Research 983(1–2): 23–35.
Taylor, D.M., S.I. Tillery, and A.B. Schwartz. 2002. Direct cortical control of 3D neuroprosthetic devices. Science 296(5574): 1829–1832.
Vaughan, T.M., D.J. McFarland, G. Schalk, W.A. Sarnacki, D.J. Krusienski, E.W. Sellers, and J.R. Wolpaw. 2006. The Wadsworth BCI Research and Development Program: at home with BCI. IEEE Transactions on Neural Systems and Rehabilitation Engineering 14(2): 229–233.
Velliste, M., S. Perel, M.C. Spalding, A.S. Whitford, and A.B. Schwartz. 2008. Cortical control of a prosthetic arm for self-feeding. Nature 453(7198): 1098–1101.
Vidal, J.J. 1977. Real-time detection of brain events in EEG. IEEE Proceedings: Special Issue on Biological Signal Processing and Analysis 65: 633–664.
Vossler, D.D., M. Doherty, R. Goodman, L. Hirsch, J. Young, and D. Kraemer. 2004. Early safety experience with a fully implanted intracranial responsive neurostimulator for epilepsy. Presented at the Annual Meeting of the American Epilepsy Society, December 2004, New Orleans, La.
Wang, W., S.S. Chan, D.A. Heldman, and D.W. Moran. 2007. Motor cortical representation of position and velocity during reaching. Journal of Neurophysiology 97(6): 4258–4270.
Williams, J.C., J.A. Hippensteel, J. Dilgen, W. Shain, and D.R. Kipke. 2007. Complex impedance spectroscopy for monitoring tissue responses to inserted neural implants. Journal of Neural Engineering 4(4): 410–423.
Wilson, J.A., E.A. Felton, P.C. Garell, G. Schalk, and J.C. Williams. 2006. ECoG factors underlying multimodal control of a brain-computer interface. IEEE Transactions on Neural Systems and Rehabilitation Engineering 14(2): 246–250.
Wolpaw, J.R., D.J. McFarland, G.W. Neat, and C.A. Forneris. 1991. An EEG-based brain-computer interface for cursor control. Electroencephalography and Clinical Neurophysiology 78(3): 252–259.
Wolpaw, J.R., and D.J. McFarland. 1994. Multichannel EEG-based brain-computer communication. Clinical Neurophysiology 90(6): 444–449.
Wolpaw, J.R., and D.J. McFarland. 2004. Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. Proceedings of the National Academy of Sciences of the U.S.A. 101(51): 17849–17854.
Wolpaw, J.R., N. Birbaumer, W.J. Heetderks, D.J. McFarland, P.H. Peckham, G. Schalk, E. Donchin, L.A. Quatrano, C.J. Robinson, and T.M. Vaughan. 2000. Brain-computer interface technology: a review of the first international meeting. IEEE Transactions on Rehabilitation Engineering 8(2): 164–173.
Wolpaw, J.R., N. Birbaumer, D.J. McFarland, G. Pfurtscheller, and T.M. Vaughan. 2002. Brain-computer interfaces for communication and control. Clinical Neurophysiology 113(6): 767–791.
Yuen, T.G., W.F. Agnew, and L.A. Bullara. 1987. Tissue response to potential neuroprosthetic materials implanted subdurally. Biomaterials 8(2): 138–141.