Brain–Computer Interface

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Brain–Computer Interface J R Wolpaw, New York State Department of Health and State University of New York, Albany, NY, USA ã 2009 Elsevier Ltd. All rights reserved.

Introduction In the nearly 80 years since Hans Berger first recorded electroencephalographic activity from the scalp, the electroencephalograph (EEG) has been used primarily for clinical diagnosis, for exploring brain function, and to a very limited extent for therapy. At the same time, many people have speculated that the EEG or other reflections of brain activity might be useful for another purpose as well: to serve as an alternative method for the brain to send messages and commands to the outside world. While the brain’s normal communication and control capabilities depend on nerves and muscles, the existence of easily recordable brain signals, such as the EEG implied the possibility of establishing nonmuscular communication and control based on brain–computer interfaces (BCIs). In spite of recurring scientific and popular interest in the idea of BCIs, and despite a few encouraging initial efforts, it is only in the past 20 years that sustained research has begun, and only in the past 12 years that a recognizable field of BCI research and development has emerged. The field is now populated by a large and rapidly growing number of research groups throughout the world. This new surge of interest and activity is due largely to the combination of four important elements. The first element is greater appreciation of the needs and the abilities of people paralyzed by disorders such as cerebral palsy, spinal cord injury, brain stem stroke, amyotrophic lateral sclerosis (ALS), and muscular dystrophies. Life-support technology (e.g., home ventilators) now enables even the most severely disabled people to survive for many years. Furthermore, it is now clear that even people who have little or no voluntary muscle control, who may be nearly ‘locked-in’ their bodies, unable to communicate in any way, can have lives that they consider enjoyable and productive if they can be given even the most basic means of communication and control. The second element is the greater knowledge of the nature and functional correlates of the EEG and other measures of brain activity that has come from animal and human research. Along with this new understanding have come improved methods for recording these signals, in both the short-term and the long-term. This increased knowledge and improved technology are directing and enabling more sophisticated and productive BCI research.

The third element is the easy availability of powerful, inexpensive computer hardware that supports the complex real-time analyses of brain activity important for successful BCI operation. Until quite recently, much of the online signal processing used in current BCIs was impossible or extremely expensive. The fourth element contributing to the rapid growth of BCI research is new appreciation of the brain’s remarkable adaptive capacities, both in normal life and in reaction to trauma or disease. The growing recognition of these adaptive capacities generates enormous excitement and interest in the potential for using them to create novel interactions between the brain and computer-based devices, interactions that can substitute for or even augment the brain’s normal neuromuscular interactions with its external and internal environments.

Definition of a BCI A BCI creates a nonmuscular output channel for the brain. Instead of being executed through peripheral nerves and muscles, the user’s wishes are conveyed by brain signals (such as those detected by an EEG), and these brain signals do not depend for their generation on neuromuscular activity. (Thus, for example, a device that uses visual-evoked potentials in the EEG to determine eye-gaze direction is not a true BCI because it relies on muscular control of eye position and simply uses the EEG as a measure of that position.) Like other communication and control systems, a BCI establishes a real-time interaction between the user and the outside world. The user gets feedback on the results of the BCI’s operation, and that feedback influences the user’s intent and the brain signals that encode that intent. For example, if a person uses a BCI to control the movements of a robotic arm, the arm’s position after each movement affects the person’s intent for the succeeding movement and the brain signals that convey that intent. Thus, a system that only records and analyzes brain signals, without providing the outcome of that analysis to the user in a real-time interactive fashion, is not a BCI. Figure 1 shows the design and operation of any BCI. Popular speculation and some scientific efforts have been based on the belief that BCIs are ‘mind-reading’ or ‘wire-tapping’ technology, devices that listen in on the brain, detect its intent, and then execute that intent. This is a misconception that ignores a central feature of the brain’s interactions with the external world. The motor skills that accomplish a person’s intent, whether it be to walk across a room, speak specific words, or play a particular piece on the

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Figure 1 The basic design of any brain–computer interface (BCI) system. Signals reflecting brain activity are acquired from the scalp, from the cortical surface, or from within the brain and are analyzed to measure signal features (such as amplitudes of evoked potentials or electroencephalograph rhythms or firing rates of single neurons) that reflect the user’s intent. These features are translated into commands that operate a device such as a word-processing program, a wheelchair, or a neuroprosthesis. Adapted from Wolpaw JR and Birbaumer N (2006) Brain–computer interfaces for communication and control. In: Selzer ME, Clarke S, Cohen LG, Duncan P, and Gage FH (eds.) Textbook of Neural Repair and Rehabilitation; Neural Repair and Plasticity, pp. 602–614. Cambridge: Cambridge University Press, with permission from Cambridge University Press.

piano, are mastered and maintained by initial and continuing ‘adaptive changes’ in brain function. In early development and throughout later life, central nervous system (CNS) neurons and synapses change continually to master new skills and to maintain those already mastered. This adaptive plasticity is responsible for basic skills such as walking and talking and for more specialized skills such as ballet, and it is guided by the results produced. Thus, as muscle strength, limb length, and body weight change with growth and aging, the CNS modifies its outputs so as to maintain its motor skills. This requirement for initial and continuing adaptation exists whether the person’s intent is carried out naturally, that is, by peripheral nerves and muscles, or through an artificial interface, a BCI, that uses brain signals instead of nerves and muscles. Successful BCI operation requires the effective interaction of two adaptive controllers: the user, who must generate

brain signals that encode intent; and the BCI system, that must translate these signals into commands that accomplish the user’s intent. Thus, BCI usage is essentially a skill that user and system work together to acquire and maintain. The user encodes intent in signal features that the BCI system can measure, and the system measures these features and translates them into device commands. This initial and continuing dependence on the mutual adaptation of user to system and system to user is a fundamental principle of BCI operation; its effective management is one of the principal challenges for BCI research and development.

Brain Signals That Could Be Used in a BCI A variety of different methodologies can detect brain activity. These include methods for recording electrical or magnetic fields, functional magnetic resonance

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imaging (fMRI), positron emission tomography (PET), and functional near-infrared (fNIR) imaging. However, magnetoencephalography, fMRI, and PET are not currently suited for everyday use due to their intricate technical demands, high expense, and/or limited real-time capabilities. Only electrical field recording (and possibly fNIR imaging) are likely to be of value for practical applications in the near future. The electrical fields produced by brain activity can be recorded at the scalp (electroencephalographic activity (EEG)), at the cortical surface (electrocorticographic activity (ECoG)), or within the brain (local field potentials (LFPs)) or neuronal action potentials (spikes). Figure 2 shows these three recording alternatives. Each method has advantages and disadvantages. EEG recording is easy and noninvasive, but it has limited topographical resolution and frequency range and can be contaminated by electromyographic (EMG) activity from cranial muscles or electrooculographic (EOG) activity. ECoG has better topographical resolution and frequency range, but it requires implantation of electrode arrays on the cortical surface, which has been done as yet for only brief periods (a few days or weeks) in people. Intracortical

recording, or recording within other brain areas, yields signals with the highest resolution, but it requires insertion of multielectrode arrays within brain tissue, and it faces as yet unresolved problems in reducing tissue damage and scarring and in achieving long-term recording stability. The ultimate usefulness of each of these methods will hinge on the range of communication and control applications it can support and on the extent to which its disadvantages can be overcome. The question of the relative value of noninvasive methods (i.e., EEG), moderately invasive methods (e.g., ECoG), and moreinvasive methods (e.g., intracortical recording) remains unanswered. It is conceivable that practical, stable, and safe techniques for long-term recording within the brain will prove relatively easy to develop. On the other hand, the information transfer rates possible with intracortical methods may turn out to be no greater than those achievable with less-invasive methods (e.g., ECoG, or even EEG). It is quite likely that different recording methods will prove useful for different applications and for different individuals. Comprehensive evaluations of the characteristics and capacities of each recording method are needed.

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5 mm Figure 2 Recording sites for electrophysiological signals used by brain–computer interface (BCI) systems. Electroencephalographic activity (EEG) is recorded by electrodes on the scalp. Electrocorticographic activity (ECoG) is recorded by electrodes on the cortical surface. Neuronal action potentials (spikes) or local field potentials (LFPs) are recorded by electrode arrays inserted into the cortex. Each recording method has advantages and disadvantages. Adapted from Wolpaw JR and Birbaumer N (2006) Brain–computer interfaces for communication and control. In: Selzer ME, Clarke S, Cohen LG, Duncan P, and Gage FH (eds.) Textbook of Neural Repair and Rehabilitation; Neural Repair and Plasticity, pp. 602–614. Cambridge, UK: Cambridge University Press, with permission from Cambridge University Press.

Human BCI experience to date consists largely of noninvasive EEG-based research. A few short-term ECoG studies have been reported, and limited data are available from a few people with intracortical implants. Most intracortical BCI data come from animals, primarily monkeys. EEG-based BCI methods can certainly support simple applications and seem to be able to support more complex ones. Invasive methods may be able to support more complex applications, but the issues of risk and long-term performance are not yet resolved. Three different kinds of EEG-based BCIs have been evaluated in people. They are distinguished by the particular EEG features they use to derive the user’s intent. Figure 3(a) illustrates a P300-based BCI. It focuses on the P300 component of the event-related brain potential, which appears in the EEG over central areas about 300 ms after a salient, or attended stimulus. In most P300-based BCIs described to date the stimulus is visual. Typically, letters, numbers, and/ or other possible choices are arranged in a matrix, and the rows and columns of the matrix flash in rapid succession. Only the row and column that contain the item that the user wants to select produce P300 potentials. By detecting these P300 potentials, the BCI system can determine the user’s selection. This BCI method is able to support operation of a simple word-processing program that enables users to

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Figure 3 Noninvasive EEG-based BCI methods that use EEG recorded from the scalp. (a) P300 evoked potential brain–computer interface (BCI). A matrix of possible selections is shown on a screen. Scalp electroencephalographic activity (EEG) is recorded over centroparietal cortex (EEG recording location PZ) while these selections flash in succession. Only the selection desired by the user evokes a large P300 potential (i.e., a positive potential about 300 ms after the flash). r 2 is the coefficient of variation; a/d u are the analog to digital conversion units. (b) Sensorimotor rhythm BCI. Scalp EEG is recorded over sensorimotor cortex. Users control the amplitudes of one or more 8–12 Hz mu rhythms or 18–26 Hz beta rhythms to move a cursor to a desired target located somewhere on a computer screen. Frequency spectra (top) for top and bottom targets indicate that this user’s control of vertical cursor movement is clearly focused in the mu-rhythm frequency band. Sample EEG traces (bottom) also show that the mu rhythm is prominent with the top target and minimal with the bottom target. Trained users can also control movement in two dimensions. Adapted from Ku¨bler A, Kotchoubey B, Kaiser J, Wolpaw JR, and Birbaumer N (2001) Brain–computer communication: Unlocking the locked-in. Psychological Bulletin 127: 358–375, with permission from American Psychological Association.

communicate at rates up to several words per minute. Improvements in signal analysis may substantially increase its capacities. Figure 3(b) illustrates a BCI based on sensorimotor rhythms. Sensorimotor rhythms are 8–12 Hz (mu) and 18–26 Hz (beta) oscillations in the EEG recorded over sensorimotor cortices. Normally, changes in mu and/or beta rhythm amplitudes accompany movement and sensation, and motor imagery as well. BCI studies show that people can learn to control mu or

beta rhythm amplitudes in the absence of any movement or sensation and that they can use this control to move a cursor to select letters or icons on a screen or to operate a simple orthotic device. Both one- and two-dimensional control are achievable. Sensorimotor rhythm-based BCIs, like P300-based BCIs, can support basic word-processing or other simple functions. They might also support multidimensional control of the movements of a neuroprosthesis or a device such as a robotic arm.

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A BCI can also use slow cortical potentials (SCPs) in the EEG, which last from 300 ms to several seconds. In normal brain function, negative SCPs accompany preparatory depolarization of the underlying cortical network, and positive SCPs are thought to reflect cortical disfacilitation or inhibition. With appropriate training, people can learn to control SCPs to produce positive or negative shifts. With this control, they can perform basic word-processing and other simple control tasks such as accessing the Internet. Current BCIs depend mainly on visual stimuli and visual feedback. However, people who are severely disabled may lack the vision or eye movements needed to perceive visual stimuli, especially if the stimuli change rapidly. Thus, BCI systems that employ auditory rather than visual stimuli would also be valuable – and are under investigation. Figure 4(a) illustrates a BCI that uses sensorimotor rhythms in ECoG recorded by electrode arrays on the cortical surface. ECoG signals are much higher in amplitude than are scalp-recorded EEG signals, they have much higher spatial and temporal resolution, and they are much less susceptible to contamination by EMG or EOG. ECoG encompasses not only mu and beta rhythms but also higher-frequency gamma (>30 Hz) rhythms, which are very small or entirely lacking in EEG. With appropriate interelectrode spacing, ECoG can resolve activity limited to a few square millimeters of cortical surface. To date, ECoG studies have been confined to short-term experiments in people temporarily implanted with electrode arrays prior to epilepsy surgery. These studies reveal sharply focused ECoG activity associated with movement and sensation and with motor imagery. Furthermore, with only a few minutes of training, people can learn to use motor imagery to control cursor movement. The speed of this learning, which appears to be faster than that usually found with sensorimotor rhythms in scalp-recorded EEG, together with ECoG’s high topographical resolution, wide spectral range, and freedom from contamination, suggests that ECoG-based BCIs might provide communication and control superior to that possible with EEG-based BCIs. Widespread clinical use of ECoG-based BCIs will require development of fully implanted systems (i.e., systems that use telemetry and thus do not have wires passing through the skin) and clear evidence that they provide safe and stable recording for years. Figure 4(b) shows a multielectrode array for intracortical recording and the locations of its implantation in human motor cortex. Intracortical BCI studies conducted in monkeys and to a limited extent in humans have shown that single-neuron activity

recorded by such arrays can be used to control movement of a cursor in one, two, or even three dimensions. It appears that LFPs, which can be recorded by the same electrode arrays and reflect nearby synaptic and neuronal activity, might provide similar control. In these intracortical single-neuron and LFP studies, the usual strategy has been to define the neuronal activity associated with standardized limb movements, then to apply this activity to simultaneously control comparable cursor movements, and finally to demonstrate that the neuronal activity alone can continue to control cursor movements in the absence of actual limb movements. As Figure 4(b) shows, the relationships between neuronal activity and cursor movements change over time. Ideally, neuronal activity adapts over sessions so as to improve cursor control. This adaptation, like the adaptations seen with EEG- and ECoG-based BCIs, illustrates the need for initial and continuing adaptation of system to user and user to system. The major questions that must be answered prior to clinical use of intracortical BCIs include their longterm safety, the stability and persistence of their signals in the face of cortical tissue reactions to the implanted electrodes, the long-term usefulness of these signals, and to what extent their capabilities in actual practical applications (e.g., in neuroprosthesis control) significantly exceed those of less-invasive BCIs. The first two websites listed provide videos that illustrate that a noninvasive EEG-based BCI using sensorimotor rhythms can provide cursor control comparable in speed and accuracy to that achieved to date with intracortical methods.

Signal Processing A BCI records brain signals and analyzes them to derive device commands. This signal processing has two parts. The first part is feature extraction, the measurement of those features of the signals that encode the user’s intent. These features can be relatively simple measures such as the amplitudes or latencies of particular evoked potentials (e.g., P300), the amplitudes or frequencies of particular rhythms (e.g., sensorimotor rhythms), or the firing rates of individual cortical neurons, or they can be more complex measures such as spectral coherences or weighted combinations of simple measures. To provide effective BCI performance, the feature-extraction part of signal processing needs to focus on features that actually do encode the user’s intent, and it needs to extract those features accurately. The second part of BCI signal processing is a translation algorithm that translates these features into device commands. Features such as rhythm

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Figure 4 Invasive brain–computer interface (BCI) methods. (a) Human electrocorticographic (ECoG) control of vertical cursor movement using specific motor imagery to move the cursor up and using rest (i.e., no imagery) to move it down. The electrodes used for online control are circled, and the spectral correlations of their ECoG activity with target location (i.e., top or bottom of screen) are displayed. Electrode arrays for Patients B, C, and D are green, blue, and red, respectively. The specific imagined actions used are indicated. The substantial levels of control achieved with different kinds of imagery are apparent. (The dashed lines indicate significance at the 0.01 level.) For Patients C and D, the solid and dotted r2 spectra correspond to the sites indicated by the dotted and solid line locators, respectively. Reproduced from Leuthardt EC, Schalk G, Wolpaw JR, Ojemann JG, and Moran DW (2004) A brain–computer interface using electrocorticographic signals in humans. Journal of Neural Engineering 1: 63–71, with permission from IOP Publishing Limited. (b) Top left: Array

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amplitudes or neuronal firing rates are translated into commands that specify outputs such as cursor movements, icon selection, or prosthesis operation. Translation algorithms can be simple (e.g., linear equations) or complex (e.g., neural networks, support vector machines). A successful translation algorithm ensures that the user’s range of control of the chosen features supports selection of the full range of device commands. Suppose, for example, that the feature is the amplitude of a 21–24 Hz beta rhythm in the EEG over left sensorimotor cortex, that the user can vary this feature over a range of 1–5 mV, and that the application is horizontal cursor movement. In this case, the translation algorithm must ensure that the 1–5 mV range permits the user to move the cursor to both the right and left edges of the screen and at a rate consistent with the rapidity and maximum duration of the user’s beta control. In addition, the algorithm must accommodate spontaneous variations in the user’s range of control (i.e., variations due to diurnal change, fatigue, or other factors). Finally, the translation algorithm should be able to adapt to at least accommodate, and at best encourage, improvements in the user’s control. Thus, if the user’s range of control improves from 1–5 to 1–8 mV, the translation algorithm should take advantage of this improvement in order to increase the speed and/or precision of cursor control. The need for ongoing adaptation of the translation algorithm to accommodate spontaneous and other changes in the signal features reflects the continuing importance of system–user and user–system adaptation and has important implications. First, it means that new algorithms cannot be evaluated adequately by off-line analyses alone. They must also be evaluated online, so that the effects of their adaptive interactions with the user can be determined. This online evaluation should be long-term as well as short-term since important adaptive interactions often develop gradually. Second, the need for ongoing adaptation means that simpler algorithms, for which adaptation is typically easier and more effective, have an inherent advantage. Simple algorithms (e.g., linear equations) should be replaced by more complex alternatives (e.g., neural networks) only after online as

well as off-line evaluations demonstrate that the more complex alternatives give superior long-term performance without continual and laborious recalibration procedures.

Potential Users In their current early stage of development, BCIs are likely to be of significant practical value primarily for people with the most severe neuromuscular disabilities, people for whom conventional assistive communication technologies, all of which require some measure of consistent voluntary muscle control, are not satisfactory options. These include people with ALS who decide to accept artificial ventilation (rather than to die) as their disease advances, children and adults with severe cerebral palsy who do not have useful muscle control, patients with brain stem strokes who have only minimal eye movement control, individuals with severe muscular dystrophies or peripheral neuropathies, and possibly people with acute disorders associated with extensive paralysis (such as Landry-Guillain–Barre´ syndrome). People with slightly less severe disabilities, such as those with high-cervical spinal cord injuries, may also find BCI technology preferable to conventional assistive communication methods that co-opt their remaining voluntary muscle control (e.g., methods that depend on gaze direction or EMG of facial muscles). The extent to which future BCIs prove useful to those with much less-severe motor disabilities will depend on the speed and precision of the control the BCI systems can provide and on the reliability and convenience of their use. People with disabilities of different kinds may differ in the BCI methods that are most useful to them. For some, the CNS deficits responsible for their disability may impair their control of certain brain signals and not others. For example, the motor cortex damage that can accompany ALS or the subcortical damage of severe cerebral palsy could conceivably impair generation or control of sensorimotor rhythms or single neuron activity. In these individuals, other brain signals (e.g., P300 potentials or neuronal activity from other brain areas) might be good alternatives.

of 100 microelectrodes chronically implanted in human motor cortex to record neuronal action potentials and local field potentials to control a cursor or other device. Top right: Position of array in human motor cortex. Reprinted by permission from Macmillan Publishers Ltd: Nature, Hochberg LR, Serruya MD, Friehs GM, et al. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. 442: 164–171; copyright 2006. Bottom: Control of three-dimensional cursor movements by single neurons in motor cortex of a monkey. The left graph shows the improvement over daily training sessions of the average correlation (r2) between the firing rate of a single cortical neuron and target direction. The right graph shows the resulting improvement in performance (assessed as the mean target radius required to maintain a 70% target hit rate). As the firing rates of the neurons that are controlling cursor movement become more strongly correlated with target direction, the size of the target can be reduced. Adapted from Taylor DM, Helms Tillery SI, and Schwartz AB (2003) Information conveyed through brain-control: Cursor versus robot. IEEE Transactions on Neural Systems Rehabilitation and Engineering 11: 195–199 (ã 2003 IEEE), with permission from IEEE.

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In this regard, it is encouraging that people severely disabled by ALS appear to retain the ability to control sensorimotor rhythms or single-neuron activity in sensorimotor cortex. Apparently trivial and prosaic factors are likely to have substantial impact on the practical success of BCI applications. Factors such as the steps required for donning and doffing electrodes or for accessing a BCI application, or the user’s appearance while operating the BCI, may greatly affect the numbers and characteristics of the people who adopt the system and the degree to which they actually use it in their daily lives.

Applications BCIs have an extensive range of possible practical applications, from very simple to very complex. Simple BCI applications have already been demonstrated in the laboratory and in limited clinical use. They include systems for answering Yes/No questions, managing basic environmental control (e.g., lights, temperature), adjusting a television, or opening and closing a hand orthosis. Such simple systems can be configured for basic word-processing, sending e-mail, or accessing the Internet. For people who are totally paralyzed (i.e., locked-in), these simple BCI applications could make it possible to lead lives that are pleasant and even productive. Indeed, many recent studies indicate that, with good supportive care and the capacity for basic communication, severely paralyzed people can enjoy what they consider to be a reasonable quality of life and are hardly more likely to be depressed than are people without physical disabilities. Thus, simple BCI applications seem to have a significant future in their capacity to improve the daily lives of those who are most severely disabled. Indeed, a few severely disabled people are already using EEG-based BCI systems for important purposes in their daily lives. More-complex BCI applications could control devices such as a motorized wheelchair, a robotic arm, or a neuroprosthesis that provides multidimensional movement to a paralyzed limb. Although present efforts focus on development of invasive BCI systems for such applications, noninvasive EEGbased BCIs also offer the possibility of such control. The eventual practical importance of such BCI applications will hinge on their capacities, practicality, and reliability, on their acceptance by specific kinds of users, and on the extent to which they have important advantages over conventional methodologies. Establishment of the clinical value and practicality of BCI applications will require thorough evaluation to demonstrate their long-term reliability, to show that people actually use the applications, and to

document that this usage has beneficial effects on factors such as mood, quality of life, and productivity. Particularly in the first stages of their development, it will frequently be necessary to configure applications that match each user’s unique needs, desires, and physical and social environments. Although the cost of BCI equipment is relatively modest, current systems need substantial and continuing expert oversight, which is extremely expensive and now obtainable only from a few research labs. As a result, these systems are not available to most potential users. Thus, the widespread dissemination of BCIs will also depend on the degree to which the need for such ongoing technical support can be minimized. BCI systems must be easy to set up, easy to use, and easy to maintain if they are to have significant practical impact on improving the lives of people with severe disabilities.

Nature and Needs of BCI Research and Development BCI research and development is a multidisciplinary effort. It requires neuroscience, engineering, applied mathematics, computer science, psychology, and rehabilitation. The need to select useful brain signals, to record them reliably, to analyze them appropriately in real-time, to control devices that provide functions valuable for those with severe disabilities, to manage the intricate short-term and long-term adaptive interactions between user and system, and to integrate BCI applications into the daily lives of their users means that the expertise and efforts of all these disciplines are essential for success. Thus, each BCI research group must incorporate all the essential disciplines, or groups with different expertise must collaborate closely. Collaborative studies are being facilitated by the widespread adoption of the generalpurpose BCI software platform BCI2000, which can readily accommodate a wide variety of different signals, processing methods, applications, operating protocols, and hardware (see third website address). Productive collaborations have also been encouraged by recent meetings drawing BCI researchers from all relevant disciplines and from all over the world, by numerous symposia and collections of BCI research presentations at larger general meetings, and by publication of extensive sets of peer-reviewed BCI articles. See also: Amyotrophic Lateral Sclerosis (ALS); Axonal Transport and ALS; Corticomotoneuronal System; Corticospinal Development; Electroencephalography (EEG); Evoked Potentials: Recording Methods; Evoked Potentials: Clinical; Map Plasticity and Recovery from Stroke; Synaptic Mechanisms of Learning.

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Further Reading Birbaumer N, Ghanayim N, Hinterberger T, et al. (1999) A spelling device for the paralyzed. Nature 398: 297–298. Donchin E, Spencer KM, and Wijesinghe R (2000) The mental prosthesis: Assessing the speed of a P300-based brain-computer interface. IEEE Transactions on Rehabilitation Engineering 8: 174–179. Hochberg LR, Serruya MD, Friehs GM, et al. (2006) Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 442: 164–171. Ku¨bler A, Kotchoubey B, Kaiser J, Wolpaw JR, and Birbaumer N (2001) Brain–computer communication: Unlocking the locked in. Psychological Bulletin 127: 358–375. Ku¨bler A, Nijboer F, Mellinger J, et al. (2005) Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface. Neurology 64: 1775–1777. Leuthardt EC, Schalk G, Wolpaw JR, Ojemann JG, and Moran DW (2004) A brain–computer interface using electrocorticographic signals in humans. Journal of Neural Engineering 1: 63–71. Pfurtscheller G and Lopes de Silva F (2005) Event-related desynchronization (ERD) and event related synchronization (ERS). In: Niedermeyer E and Lopes daSilva FH (eds.) Electroencephalography: Basic Principles, Clinical Applications and Related Fields, pp. 1003–1016. Baltimore: Williams and Wilkins. Robbins RA, Simmons Z, Bremer BA, Walsh SM, and Fischer S (2001) Quality of life in ALS is maintained as physical function declines. Neurology 56: 442–444. Schalk G, McFarland DJ, Hinterberger T, Birbaumer N, and Wolpaw JR (2004) BCI2000: A general-purpose brain–computer interface (BCI) system. IEEE Transactions on Biomedical Engineering 51: 1034–1043.

Taylor DM, Helms Tillery SI, and Schwartz AB (2003) Information conveyed through brain-control: Cursor versus robot. IEEE Transaction on Neural System Rehabilitation and Engineering. 11: 195–199. Taylor DM, Tillery SI, and Schwartz AB (2002) Direct cortical control of 3D neuroprosthetic devices. Science 296: 829–832. Vaughan TM and Wolpaw JR (eds.) (2006) The Third International Meeting on Brain-Computer Interface Technology: Making a difference IEEE Transactions on Neural Systems Rehabilitation Engineering 14(2): 126–127. Wolpaw JR and Birbaumer N (2006) Brain–computer interfaces for communication and control. In: Selzer ME, Clarke S, Cohen LG, Duncan P, and Gage FH (eds.) Textbook of Neural Repair and Rehabilitation; Neural Repair and Plasticity, pp. 602–614. Cambridge, UK: Cambridge University Press. Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, and Vaughan TM (2002) Brain–computer interfaces for communication and control. Clinical Neurophysiology 113: 767–791. Wolpaw JR and McFarland DJ (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 United States of America 101: 17849–17854.

Relevant Websites http://www.bci2000.org – BCI 2000 (brain–computer interface research project). http://www.nature.com – Nature Supplementary Information (Supplementary Video 1 download). http://www.bciresearch.org – QuickTime video (‘Two-Dimensional Cursor Control With Scalp-Recorded Sensorimotor Rhythms’) from the BCI Group, Wadsworth Center, Albany, NY.