INTRODUCTION

Modern research on brain–machine interfaces (BMI) is a highly multidisciplinary field that has been developing at a stunning pace since the first experiment conducted 8 years ago that demonstrated direct control of a robotic manipulator by ensembles of neurons recorded in cortical and subcortical areas in awake, behaving rats (Chapin, Moxon et al. 1999). Since this pioneering study, an exponentially growing stream of research publications has provoked an enormous interest in BMIs among scientists from different fields and the lay public. This level of interest stems from both the use of BMIs to investigate the way large and distributed neural circuits operate in behaving animals and the perceived potential that BMI technology can realize for restoration of motor behaviors and other functions in patients suffering from devastating neurological conditions.

In theory, the group of patients that can benefit from BMI systems includes people who lost mobility as a consequence of neurodegenerative disorders, such as amyotrophic lateral sclerosis (ALS), severe trauma and irreversible spinal cord injuries, stroke, and cerebral palsy. As the risk–benefit factor of invasive BMIs improves, it is conceivable that the same technology may be accepted by patients with less severe degrees of body paralysis or even by amputees.

Future BMI technologies will not be limited to systems for restoration of mobility. We expect that systems for restoration of speech and restoration of communication between brain areas will likely emerge. These future neuroprostheses are expected to be seamlessly integrated with the human body as much as possible and to use the most advanced developments in robotics, material science, computational algorithms, and electrical engineering.

Notwithstanding these high expectations, much work has to be done to develop solutions for numerous issues that preclude an immediate translation of laboratory demonstrations into practical clinical applications. Most of BMI research has been conducted in monkeys and rats, and clinical trials in humans are only starting. In this chapter, we highlight the major obstacles faced by BMI research and lay out a roadmap that can transform experimental advances into clinical applications that will benefit millions of people worldwide in the next decade. This roadmap is based on a critical analysis of previous studies conducted in both experimental animals and human subjects. The milestones that we propose take into account the experience accumulated during the last 5 years by a multiuniversity consortium led by the Duke University Center for Neuroengineering.

NONINVASIVE BMIS

The first distinguishing feature of the BMIs (also called brain–computer interfaces, BCIs, if they involve mostly user–computer interactions) is whether they are based on invasive or noninvasive recordings. The majority of noninvasive BCIs employ electroencephalographic (EEG) recordings to derive the neuronal signal to be used in the control of computer cursors or other devices. This approach has dominated human studies conducted during the last decade. These studies helped to develop useful tools for helping severely paralyzed patients to communicate with the outside world (Birbaumer, Ghanayim et al. 1999; Kubler, Kotchoubey et al. 2001; Kubler, Neumann et al. 2001; Obermaier, Neuper et al. 2001; Wolpaw, Birbaumer et al. 2002; Obermaier, Muller et al. 2003; Sheikh, McFarland et al. 2003; Wolpaw 2004; Hinterberger, Veit et al. 2005). EEG-based BCIs have an obvious advantage of not exposing the patients to the risks of invasive surgical procedures. Despite this advantage, communication channels formed by such systems have low definition, with typical transfer rate on the order of 5–25 bits/s (Wolpaw, Birbaumer et al. 2002; Birbaumer 2006). Such low resolution may not be satisfactory for advanced neuroprosthetic devices intended for control of a multidegree of freedom actuators with the functionality approximating that of the human arm and hand. Advanced systems for speech restoration need communication channels with much higher transfer rate, as well. Based on these considerations, we predict that, in the future, safe and efficient invasive systems will outperform the ones based on the low-resolution EEG recordings. However, this will likely be a long transition, and the EEG-based technologies will continue to help patients before invasive BMIs become mature enough for clinical use.

EEGs as means to provide human subjects with a biofeedback of their brain activity came into the scope of neurological and neurophysiological research in the 1960s and 1970s. In these studies, both operantly conditioned experimental animals and human subjects demonstrated an amazing ability to gain control of their own brain. Nowlis and Kamiya et al. reported that using EEG biofeedback human subjects had developed an ability to voluntarily control α rhythms of the occipital lobe (Nowlis and Kamiya 1970). Other investigators conducted similar research of the rhythms detected in different brain areas. For example, Sterman et al. described operant conditioning of the sensorimotor μ rhythm in cats (Wyricka and Sterman 1968) and human subjects (Sterman, Macdonald et al. 1974). These early studies were very important for the development of both invasive and noninvasive BMIs because they demonstrated that the brain can voluntarily control its own activity if provided with a biological feedback. The next step was to make use of this ability in a system that links the brain to an external actuator.

EEG-based BCIs make an attempt to read out the subjects’ thoughts, intentions, and decisions from the massive electrical activity of neuronal populations. However, EEGs have limited spatial and temporal resolution because they comprise neural activity of several cortical areas, which is distorted (low-pass filtering) during conductance through the tissue that separates the signal sources and the recording electrodes: brain tissue, bone, and skin. EEG recordings are highly susceptible to electrical (50 or 60 Hz) artifacts, and the artifacts related to electromyographic (EMG) activity of facial muscles and eye movements. Mechanical artifacts interfere with EEG recordings, as well. Because of these issues, EEG-based BCIs are not practical for everyday activities. Most successful clinical implementations of these systems have been in severely paralyzed, “locked in” patients who benefited even from the simplest ways to communicate with the outside environment.

Generally, EEG techniques can distinguish modulations of brain activity correlated with external stimuli conveyed by different modalities, gaze angle, cognitive states, and voluntary intentions. The BCIs that use EEGs as their inputs attempt to extract such modulations from the otherwise noisy signals. The BCI systems proposed so far can be distinguished by the cortical areas recorded, sensory modality providing a feedback, and the features of EEG signals extracted. Some BCIs rely on the detection of neuronal responses to external stimuli (evoked potentials), typically presented to the subjects as items on a computer screen. The underlying idea is that the EEG activity is different depending on the stimulus that the subject selects as his or her choice. For example, BCIs that make use of visual evoked potentials (VEPs) recognize the subject’s selection by detecting the VEP that occurs when the subject visually fixates the selected item (Sutter and Tran 1992; Middendorf, McMillan et al. 2000) or attends to it (Kelly, Lalor et al. 2005). Flickering stimuli are often used in such implementations. P300-based BCIs operate on a similar principle. They recognize the subjects’ selections by detecting elevated evoked potentials in the parietal cortex that occur during the presentation of the preferred stimuli (Donchin, Spencer et al. 2000; Piccione, Giorgi et al. 2006; Sellers and Donchin 2006).

A separate class of EEG-based BCIs generates continuous output signals that drive computer cursors. Several principles of operation have been suggested for such systems. Some groups suggested using slow cortical potentials (Birbaumer, Kubler et al. 2000), whereas others based their BCIs on the faster μ (8–12 Hz) and β (18–26 Hz) (Pfurtscheller, Neuper et al. 2003; Wolpaw and McFarland 2004; Pfurtscheller, Brunner et al. 2006). In an attempt to direct the cursor to a particular location on the screen, the subjects often use motor imagery, for example, imagine moving the foot or hand. One group has developed computer algorithms that detect event-related synchronization and desynchronization of the EEGs associated with such imagery (Pfurtscheller and Lopes da Silva 1999; Pfurtscheller, Neuper et al. 2003).

It typically takes the subjects many days of training to acquire ability to control an EEG-based BCI (Wolpaw, Birbaumer et al. 2002), although shorter training times have been reported recently (Wolpaw and McFarland 2004). Visual feedback constitutes an essential part of training to use a BCI. Some BCI designs take advantage of the brain plasticity that takes place during learning. Other designs attempt to recognize the EEG patterns related to particular intentions of the subjects. Classifier algorithms of different complexity have been suggested for this task. Advanced BCIs (Wolpaw and McFarland 2004) implement adaptive algorithms that make use of both the classifier and brain plasticity. These systems constantly modify the classifier parameters while the subjects train to operate the BCI. Visual feedback can be delivered very effectively using virtual reality systems that immerse the subject in a realistic sensory environment (Bayliss and Ballard 2000). Recently, virtual reality feedback was used in a BCI in which the subjects navigated through visual scenes by imagining themselves walking (Pfurtscheller, Leeb et al. 2006).

The resolution of the EEGs being low, a recent trend in this field is to employ sub-dural recordings of electrocorticograms (ECoGs), an invasive method that yields a signal with better resolution. This technique samples electrical activity from the smaller cortical areas compared to scalp EEGs. In addition, it has better temporal resolution. For instance, ECoGs can record γ rhythms (>30 Hz), which are not easily detectable by the EEGs. It was reported that ECoG-based BCIs are more accurate than the EEG-based systems, and the subjects train faster (Leuthardt, Schalk et al. 2004).

EEG-based BCIs have been tested in both normal subjects and patients. These tests demonstrated that such systems work in providing simple communication channels, but at the same time exposed their limitations in more complex operations. Normal subjects and neurologically disabled patients were able to control computer cursors using the EEG-based BCIs, and in some cases they enacted movements in artificial devices. The cursors were often used to indicate the users’ selections, for example, selections of letters in a spelling device. A spelling device based on slow cortical potentials was the first successful application of this type (Birbaumer, Ghanayim et al. 1999). BCIs that use slow potentials as their input continue to be developed (Hinterberger, Kubler et al. 2003). In addition to slow potentials, BCIs based on μ and β rhythms were tested in severely paralyzed patients (Kubler, Nijboer et al. 2005). In one successful study, a tetraplegic patient gained the ability to grasp objects using a motor-imagery BCI that recognized β waves in his sensorimotor cortex and put in action a functional electrical stimulator connected to his paralyzed hand (Pfurtscheller, Muller et al. 2003). In another clinical trial, a partially paralyzed patient controlled a motor-imagery-based BCI (Kubler, Nijboer et al. 2005) that was coupled to an implanted neuroprosthesis system (Freehand ©; Keith, Peckham et al. 1989). P300-based BCIs were implemented in tetraplegic patients (Piccione, Giorgi et al. 2006) and in patients with amyotrophic lateral sclerosis (Sellers and Donchin 2006). These experiments demonstrated that patients with communication and motor deficits can restore some of the lost functions using EEG-based systems.

INVASIVE BMIS

Invasive BMIs have been tested mostly in laboratory animals. Only a few studies in human subjects have been conducted (Patil, Carmena et al. 2004; Hochberg, Serruya et al. 2006). This approach is based on the recordings of high-quality signals from the ensembles of single neurons. Pioneering studies on biofeedback based on single-cell activity were conducted by Fetz et al. in the 60s and 70s (Fetz 1969; Fetz and Finocchio 1971; Fetz and Finocchio 1972; Fetz and Baker 1973; Fetz and Finocchio 1975; Fetz 1992). They showed that monkeys can learn to voluntarily control the activity of their cortical neurons if they are provided with a biofeedback that indicates the level of neuronal activity. A few years after these studies, Edward Schmidt proposed that cortical neural activity can be used as a source of voluntary motor commands to a prosthetic device for restoration of motor functions in patients with paralysis (Schmidt 1980).

Experimental testing of Schmidt’s proposal took almost 20 years to come true because of the technical difficulties associated with the complex task of recording from large ensembles of cortical cells and analyzing these vast amounts of information in real time. Solving these problems was possible because of a series of breakthroughs in technology and experimental approaches that led to an advanced methodology for chronic, multisite, multielectrode recordings from large populations of single neurons (Nicolelis, Baccala et al. 1995; Nicolelis, Ghazanfar et al. 1997; Nicolelis 2001; Nicolelis and Ribeiro 2002; Nicolelis, Dimitrov et al. 2003). In the first experimental demonstration of a BMI, neuronal population activity recorded in multiple brain areas of behaving rats controlled the one-dimensional movements of a robotic device (Chapin, Moxon et al. 1999). Importantly, it was shown that the rats could control the robot without performing any overt movements with their body parts. This first demonstration triggered a large number of follow-up studies. BMI approaches based on single-neuron recordings were soon implemented in primates: first in New World (Wessberg, Stambaugh et al. 2000), and then in rhesus monkeys (Serruya, Hatsopoulos et al. 2002; Taylor, Tillery et al. 2002; Carmena, Lebedev et al. 2003; Lebedev, Carmena et al. 2005). As a result of these initial experiments, several laboratories developed BMIs that reproduced arm-reaching trajectories (Chapin, Moxon et al. 1999; Wessberg, Stambaugh et al. 2000; Serruya, Hatsopoulos et al. 2002; Taylor, Tillery et al. 2002; Carmena, Lebedev et al. 2003; Lebedev, Carmena et al. 2005) and the combination of reaching and grasping movements (Carmena, Lebedev et al. 2003), using either computer cursors or robotic manipulators as actuators.

Up to date, the majority of the studies on the invasive BMIs have been conducted in rhesus monkeys. The proposed BMIs can be distinguished by several features: the number of electrode arrays implanted in monkey cortex; cortical sites implanted; characteristics of neural signal recorded, and the size of the neuronal population sampled. The approach of the Duke University team is based on multisite recordings from large neuronal ensembles. Other groups suggested a single cortical site recordings from small neuronal samples (less than 30 neurons) (Serruya, Hatsopoulos et al. 2002; Taylor, Tillery et al. 2002; Tillery and Taylor 2004). Some of these groups have focused on the recordings from the primary motor cortex (Serruya, Hatsopoulos et al. 2002; Taylor, Tillery et al. 2002), whereas one group has chosen posterior parietal cortex as the primary source for inputs for their decoding system (Musallam, Corneil et al. 2004).

The design of the Duke University BMI, based on long-term recordings from large populations of neurons (100–400 units), has its origin in the early studies of our laboratory that were carried out in 1995. In these experiments, chronic, multi-site, multielectrode recordings in freely behaving rats were pioneered, which allowed extracellular recordings of all major processing levels of the somatosensory system (Nicolelis, Baccala et al. 1995). A series of studies followed this demonstration in which ensemble encoding of tactile stimuli in the somatosensory system of the rat was uncovered using computational algorithms for pattern recognition, such as artificial neural networks (Ghazanfar, Stambaugh et al. 2000; Krupa, Wiest et al. 2004). This design was then moved to BMI experiments in rats (Chapin, Moxon et al. 1999) and monkeys (Wessberg, Stambaugh et al. 2000; Carmena, Lebedev et al. 2003; Lebedev, Carmena et al. 2005). Currently, we are investigating the clinical relevance of this approach. We have already completed one clinical study in which multi-electrode recordings were conducted during the intraoperative placement of deep brain stimulators in Parkinsonian patients (Patil, Carmena et al. 2004). We found that the decoding algorithms as employed in our previous studies of the BMIs in monkeys can successfully extract task-related signals from neural activity recorded in the subthalamic nucleus and the thalamus.

PRINCIPLES OF BMI OPERATION

BMIs that are based on neuronal ensemble recordings convert the activity of many cortical neurons into output signals that are useful for controlling artificial actuators. Such decoding makes use of the property of individual neurons located in motor areas to modulate their activity in relationship to movements. In addition, task-related modulations in other cortical and subcortical areas can be used to control BMIs. Task-related modulations of the firing rate of single neurons in monkey motor cortex were first described by Evarts and colleagues four decades ago (Evarts 1966; Evarts 1968a; Evarts 1968b). These initial studies and the studies that followed discovered that firing modulation in single cells were variable from trial to trial (Cohen and Nicolelis 2004; Wessberg and Nicolelis 2004; Carmena, Lebedev et al. 2005; Stein, Gossen et al. 2005). The modulation of a single neuron firing rate can change substantially from one trial to the next even if the overt movements remain virtually identical. However, fairly consistent firing patterns are revealed by averaging the activity of large populations of single neurons across many trials. Discharges of individual neurons fluctuate around these average values. The main idea of the BMI based on large neuronal ensembles is that averaging across large populations of neurons significantly reduces the variability of the population signal (Wessberg, Stambaugh et al. 2000; Carmena, Lebedev et al. 2005).

Processing of neural signals by the BMIs can be divided into several stages. First, high-quality signals emanating from many neurons have to be recorded by an implant placed in the brain area of interest. Second, motor control signals have to be extracted from the recorded firing patterns of neuronal populations. Third, the extracted control signals have to enact motor behaviors in an artificial actuator, for example, a prosthetic device. Finally, the information about the actuator performance has to be delivered back to the subjects in some sort of feedback so that errors in performance could be corrected. The subject’s brain is an important component of these control loops not only because it provides the signals that control the actuator movements and analyzes the feedback information but also because its neural circuits can undergo plastic changes to improve the performance. An invasive BMI based on the decoding of neuronal ensemble activity can be accepted by patients only if it is capable of reliable performance. It is also important that the prosthetic devices aided by BMIs feel and act as the subjects’ own limbs. Our recent results suggest that advanced neuroprostheses of the limbs can be incorporated in the internal representation of the body maintained by the brain by creating conditions under which the brain can undergoes experience-dependent plasticity. In the majority of experiments, plastic adaptation of the brain was achieved with the help of visual feedback. In our opinion, an even more efficient way to incorporate the prosthesis in the brain representation of the body could be to use multiple feedback signals derived from pressure and position sensors placed on the prosthetic limb and delivered back to the brain through the somatosensory modality. Such feedback signals are expected to create a realistic perception of the ownership of the prosthetic limb. Moreover, we predict that somatosensory feedback can be delivered to the brain using microstimulation of cortical somatosensory areas. Long-term operation of a sensorized prosthesis will then evoke cortical plasticity resulting in the dedication of sensorimotor and associative areas of the brain to representing the prosthetic device as if it were a natural body appendage.

PROBLEMS FACED BY INVASIVE BMIS

In order to fulfill the ambitious goal of creating a clinically useful invasive BMI for restoring upper limb mobility, the BMI research has to resolve several key bottlenecks. First, the stability of neuronal recordings has to be improved. In the perspective, BMIs intended for clinical applications have to allow recordings of large populations of neurons for many years without deterioration of recording quality. The number of recorded neurons has to be in the thousands. To achieve this task, a new generation of biocompatible 3-D electrode matrices will have to be developed. Such electrode arrays will have thousands of recording channels while producing minimal inflammatory reaction and brain tissue damage. Second, next-generation computational algorithms have to be developed. Such algorithms will flexibly reconstruct the activity of large neuronal populations under a range of conditions and allow stable and accurate performance of the neuroprostheses. One challenging task is the development of computational algorithms for controlling multiple-degree-of-freedom artificial actuators. Third, the property of the brain to undergo plastic changes has to be fully utilized in the design of prosthetic devices. Fourth, a new generation of upper limb prosthetics will have to be engineered that will allow light and convenient-to-use prosthetic limbs to operate under different load conditions.

IMPLANTABLE DEVICES FOR RECORDINGS FROM LARGE NEURONAL ENSEMBLES

Experiments in the monkeys showed that high-yield recordings from large populations of neurons can be achieved using implantable microwire arrays. Such implants are characterized by excellent recording quality from several months to several years, depending on the monkey species involved in the study. Traditionally, neurophysiologists prefer to record from single neurons. However, our analyses showed that multiunit signals that comprise activity of several neurons can provide a highly efficient information channel as well (Carmena, Lebedev et al. 2003). In addition to neuronal activity, local field potentials (LFPs) can be used as the source of neural information (Pesaran, Pezaris et al. 2002; Mehring, Rickert et al. 2003; Rickert, Oliveira et al. 2005; Scherberger, Jarvis et al. 2005). We expect that advanced neuroprosthetic devices will record and process different types of signals. Here, we discuss the issues related to using single- and multiunit signals as the BMI input.

One fundamental question in the BMI design concerns the number of neurons that can efficiently control a neuroprosthetic device for restoration of motor control. Different opinions on this matter have been expressed. Some groups (Serruya, Hatsopoulos et al. 2002; Taylor, Tillery et al. 2002; Taylor, Tillery et al. 2002) have strongly claimed that recordings from a small number of neurons can be sufficient for good performance of a BMI. In their experiments, rhesus monkeys controlled relatively simple movements of computer cursors. Therefore, these results cannot be generalized to include more complex movements, especially those approaching the multiple-degree-of-freedom functionality of the human arm. In addition, the claim that small neuronal samples are sufficient for good BMI performance are evidently related to technical difficulties of recordings from large populations of neurons. In our offline analysis of large populations of neurons recorded in the cortex of rhesus monkeys that operated a BMI for reaching and grasping movements, selected populations of highly tuned neurons could predict movement parameters (Sanchez, Carmena et al. 2004). However, those were the neurons specifically selected to maximize the signal-to-noise ratio (SNR) of the population. In a typical experimental situation, highly tuned neurons constitute only a small proportion in a random sample of cortical cells detected by the implanted electrodes. Thus, it would be unrealistic to expect that, in a neuronal population recorded by a small number of implanted electrodes, a large fraction of cells could be highly tuned to a particular motor parameter. It would be even more unrealistic to expect that small numbers of neurons would be useful for decoding several parameters simultaneously. From these basic considerations we conclude that large neuronal samples are always preferable. Large samples of brain cells are needed, at the very least, to produce a neuronal pool from which sufficient number of highly tuned neurons can be drawn. In addition to this obvious point, there are additional reasons for relying on large neuronal populations. One advantage that the large samples of neurons have compared to small samples is their high reliability, which improves considerably with the number of simultaneously recorded neurons (Carmena, Lebedev et al. 2005). The predictions of behavioral variables obtained from small ensembles suffer from instability and drifts, whereas in the large ensembles this problem is minimized. A less understood issue is the adaptation of cortical neurons during a long-term utilization of a BMI. Depending on the decoding algorithms, selected subsets of recorded neurons can exhibit specific adaptations. In our opinion, using large neuronal samples in BMI control provides excellent opportunity to detect the neurons with particular plastic properties and selectively adapt the prediction models to accommodate these neurons. In addition, we have frequently observed that the “best neuron” of today’s session might not be as good for BMI control the day after. Because of this effect, the BMI needs to have access to as many neurons as possible. We conclude that it is highly unlikely that a small group of neurons will be sufficient to control advanced neuroprostheses developed for human use.

One important challenge that the BMI faces is the problem of biocompatibility (Schultz and Willey 1976; Dodson, Chu et al. 1978; Landis 1994; Berry M 1999; Tresco, Biran et al. 2000; Polikov, Tresco et al. 2005). Currently, chronically implanted microwire arrays allow good quality of recordings in experimental animals (Wessberg, Stambaugh et al. 2000; Nicolelis 2001; Carmena, Lebedev et al. 2003; Nicolelis 2003; Nicolelis, Dimitrov et al. 2003; Lebedev, Carmena et al. 2005). Whereas a typical monkey experiment runs for several months (in some cases year), BMIs intended for human use have to operate reliably for many years. First and foremost, the broad and challenging issue of biological compatibility has to be properly addressed and solved. In rhesus monkey experiments, recording quality often deteriorates, which likely happens because of electrode encapsulation by fibrous tissue and cell death in the vicinity of the electrode (Polikov, Tresco et al. 2005). Several measures have been suggested to cope with encapsulation. One way to improve biocompatibility is using cone electrodes that contain neurotrophic medium (Kennedy 1989; Kennedy, Mirra et al. 1992; Kennedy and Bakay 1998; Kennedy, Bakay et al. 2000). In addition, coating of the electrodes with biochemical factors that promote neuronal growth (nerve growth factor (NGF), brain-derived neurotrophic (BDNF), laminin) and various antiinfiammatory compounds (e.g., dexamethazone) (Ignatius, Sawhney et al. 1998; Cui, Lee et al. 2001; Rahimi and Juliano 2001; Kam, Shain et al. 2002; Biran, Noble et al. 2003; Cui, Wiler et al. 2003; Polikov, Tresco et al. 2005) have been suggested. These promising approaches will need much testing and development before the optimal biocompatible electrode is designed.

We expect that, in parallel with the efforts to resolve the biocompatibility issues, new 3-D electrode matrices will be developed. These advanced recording systems will increase the number of simultaneously recorded neurons from hundreds to thousands. Human safety is the primary issue that should be taken into account in these developments. Traditional arrays of rigid, single-ended electrodes may not be adequate for clinical applications for several reasons. Such arrays can sample neuronal activity from flat surfaces of the cortex, but are not well suited for the recordings from cortical sulci. In addition, their reliability is jeopardized by the routine that involves plugging and unplugging of external headstages and the use of cables. Such operations are common in monkey experiments. However, they carry a significant risk of damaging the implant, which is unacceptable for clinical applications. We envision a neuroprosthetic of the future as a fully implantable electronic device for amplification of a large number of neuronal signals equipped with a wireless link that subserves its communication with the computing devices and external actuators. This goal is the major technological challenge that will determine the success of invasive BMIs in clinical applications intended for restoration of neurological functions in patients. The development of telemetry (Claude, Knutti et al. 1979; Knutti, Allen et al. 1979; Mackay 1998; Chien and Jaw 2005; Mohseni, Najafi et al. 2005) intended for multichannel transmission (Bossetti, Carmena et al. 2004; Morizio et al. 2005) is underway and will need thorough testing in animal experiments followed by clinical trials in humans.

In addition to traditional approaches, many novel ideas of how to improve recording techniques have been proposed. These ideas consider ceramic-based multielectrode arrays (Moxon, Leiser et al. 2004) and even nanotechnology probes that access the brain through the vascular system (Llinas, Walton et al. 2005). Much research will be needed to select the viable ones from these proposals.

ALGORITHMS FOR PROCESSING NEURONAL ACTIVITY

The neuronal mechanisms through which motor and cognitive information is processed in the mammalian brain are far from being completely understood. Rate encoding, temporal encoding, and population encoding principles have been suggested in the literature as ways for information to be represented by neural circuits. Many of these ideas continue to be tested in neurophysiological experiments.

Overall, neurophysiological results provide a wealth of information for BMI implementations. Thorough knowledge of the computations carried out by populations of neurons, however, is not totally critical to the design of clinically useful BMIs. That is because BMI systems typically take advantage of a correlation between the discharges of cortical neurons and motor parameters of interest. Thus, BMI computational algorithms perform a reverse operation: they predict motor parameters from the patterns of neuronal firing. Although motor parameters are being predicted, this does not necessarily mean that the neurons from which such predictions are derived normally have a causal relation with the generation of movements. Indeed, such a relation can be quite complex. However, chronic utilization of BMIs in animals seem to be capable of inducing a causal relationship between the neuronal firing and the actuator movements. This new relationship emerges as a result of a process of experience-dependent neuronal plasticity that accompanies BMI operation. One type of correlational relationship between neuronal activity and movement utilized in BMI design is directional tuning (Georgopoulos, Schwartz et al. 1986; Georgopoulos, Kettner et al. 1988). In addition, BMI algorithms have also taken advantage of more general spatiotemporal correlation of neuronal activity with kinematic parameters (Ashe and Georgopoulos 1994; Moran and Schwartz 1999; Averbeck, Chafee et al. 2005) and kinetic (Sergio and Kalaska 1998; Todorov 2000; Sergio, Hamel-Paquet et al. 2005).

Since the first experimental demonstration in rats, a great number of linear and nonlinear algorithms for translating neuronal activity into commands to move artificial actuators have been suggested (Chapin, Moxon et al. 1999; Wessberg, Stambaugh et al. 2000; Taylor, Tillery et al. 2002; Carmena, Lebedev et al. 2003; Kim, Sanchez et al. 2003; Brockwell, Rojas et al. 2004; Brown, Kass et al. 2004; Kemere, Shenoy et al. 2004; Wessberg and Nicolelis 2004; Wu, Black et al. 2004; Hu, Si et al. 2005; Truccolo, Eden et al. 2005). Interestingly, rather simple multiple linear regression models appear to be very efficient in many BMI designs and often outperform more complicated methods (Wessberg, Stambaugh et al. 2000; Serruya, Hatsopoulos et al. 2002; Carmena, Lebedev et al. 2003; Patil, Carmena et al. 2004; Lebedev, Carmena et al. 2005; Santucci, Kralik et al. 2005). A linear model represents predicted parameters (limb position and velocity, muscle force, etc.) as weighted sums of neuronal rates. Neuronal rates are measured at different time points in the past (typically, up to 1s in the past). The number of input variables in a linear model and the size of the time window used for predictions can be optimized for each parameter (Wessberg and Nicolelis 2004; Santucci, Kralik et al. 2005). To cope with overfitting, it is often advantageous to remove noisy inputs from the model and use only the neurons that are highly correlated with the parameter of interest (or task-related, as neurophysiologists usually say) (Sanchez, Carmena et al. 2004). Linear methods can continuously adapt as long as the subjects train with the BMI (Taylor, Tillery et al. 2002). To simultaneously predict multiple motor parameters, several linear models are set to run in parallel. Using this algorithm, we predicted arm position and velocity and hand-gripping force while the monkey operated a BMI that controlled reaching and grasping movements in a robotic manipulator (Carmena, Lebedev et al. 2003). Importantly, the predictions of multiple parameters were obtained from the same population of neurons. This finding supports the theory of parallel, distributed processing that postulates simultaneous processing of multiple variables by the overlapping neuronal ensembles. In such processing scheme, one population of neurons can predict several parameters, and a single cortical neuron contributes to several predictions.

Using large-scale recordings from neuronal ensembles located in multiple cortical areas, a wide range of information can be extracted from neural circuits—both the information that the subject is consciously aware of and the information processed implicitly and subconsciously. As such, we expect that, in the future, neuron ensemble-based BMIs will by far outperform the ones based on EEG recordings.

The choice of parameters extracted in each future clinical implementation of BMIs will depend on the rehabilitation or therapeutic goals of these applications. For example, our experimental BMI for reaching and grasping (Carmena, Lebedev et al. 2003) can be used as a prototype of human neural prosthesis intended for restoration of the basic capacity of reaching and grasping in paralyzed patients. Such devices can be helpful for quadriplegic or “locked-in” patients to perform basic exploration and manipulation of objects in their surrounding space. However, the tasks of future neuroprostheses will not be limited to limb motor control. We envision the development of BMIs that one day could synthesize speech in aphasic patients using neuronal signals recorded in intact regions related to speech generation. Moreover, BMIs could conceivably be used to restore communication between parts of the central and peripheral neural system affected by neurological diseases.

One promising system is the BMI that predicts EMG-like signals (Santucci, Kralik et al. 2005). This design has the benefit of being able to control biologically inspired devices. Such devices can produce a whole range of actuator stiffness. Stiffness of a robotic apparatus is an important property needed for a future generation of practical prostheses of the limbs intended for manipulation of very different objects. For instance, such a device will be able to throw a ball and use a pen to write a signature—very different mechanical operations that require specific control modes. Another intriguing application for BMIs that decode muscle activation-like signals is implementing direct stimulation of muscles that paralyzed patients cannot activate voluntarily (Degnan, Wind et al. 2002; Navarro, Krueger et al. 2005; Peckham and Knutson 2005). Such BMIs are likely to be of great benefit to patients, especially because they can be entirely encased in the patient’s body.

In addition to decoding motor parameters (Chapin, Moxon et al. 1999; Wessberg, Stambaugh et al. 2000; Serruya, Hatsopoulos et al. 2002; Taylor, Tillery et al. 2002; Santucci, Kralik et al. 2005), decoding cognitive signals and using them to control BMIs is a promising idea for future developments. BMIs that decode intended reach direction during the delay periods preceding movement execution have been proposed (Shenoy, Meeker et al. 2003; Hatsopoulos, Joshi et al. 2004; Musallam, Corneil et al. 2004; Rizzuto, Mamelak et al. 2005). It should be noted that BMIs based exclusively on cognitive signals cannot execute continuous control of movement parameters. Instead, they decode higher-order parameters, such as prepared reach direction or characteristics of objects that potentially can be grasped. For such decoding algorithm to work, higher-order details of motor execution have to be delegated to the actuator controller.

Recently, we have examined a mode of operation that we called a shared control mode (Kim, Biggs et al. 2005). Our analyses showed that shared control can improve the accuracy with which the robotic device implements reaching and grasping movements. In an advanced neural prosthesis, a shared-control mode of operation would be achieved by an algorithm that extracts high-order signals from the brain and sends them to a low-level controller that uses artificial “reflex-like” circuits to improve the precision of the prosthetic limb movements.

Cognitive variables that advanced BMIs can utilize remain largely unexplored. We predict that new BMI designs will incorporate such higher-order neuronal representations of movements as encoding of movement sequences (Hoshi and Tanji 2004; Lu and Ashe 2005), reference frames (Graziano and Gross 1998; Batista, Buneo et al. 1999; Battaglia-Mayer, Ferraina et al. 2000; Olson 2003), and potential movement targets (Cisek and Kalaska 2002). We expect that BMIs will learn to simultaneously manipulate multiple spatial variables such as movement direction, orientation of selective spatial attention, and gaze angle (Boussaoud, Barth et al. 1993; Lebedev and Wise 2001).

One unexplored area is the implementation of BMIs for the encoding of temporal characteristics of movements. Neurophysiological, neuropsychological, and imaging studies (Ivry 1996; Leon and Shadlen 2003; Matell, Meck et al. 2003; Roux, Coulmance et al. 2003) point to a rather distributed representation of temporal information in the brain that includes cortical, thalamic, and striatal circuitry. Recently, we approached this issue by examining neuronal recordings obtained from primary motor and premotor cortex while rhesus monkeys performed self-timed button presses (O’Doherty, Lebedev et al. 2005). Neuronal ensembles accurately predicted both the time that elapsed since the monkey pressed the button and the time until the button was released. Currently, we are adding this type of information into our newest BMI systems. We expect that such BMIs will be able to mimic the temporal structure in which episodes of movement execution are intermingled with periods of immobility.

BRAIN PLASTICITY INVOLVED IN BMI OPERATIONS

Ideally, a prosthetic device directly controlled by brain activity should look and feel as the subject’s own limb. In theory, this ambitious goal can be attained thanks to the remarkable ability of the brain to plastically adapt to new motor and sensory requirements. It is currently believed that the brain contains a supramodal representation of the body, which is often termed the “body schema.” Almost 100 years ago, Head and Holmes suggested that the “body schema” could extend itself to include a wielded tool (Head and Holmes 1911). Controlling an artificial actuator through a BMI is a process somewhat similar to the operation required by subjects to manipulate tools—a capacity that endows only higher primates, such as chimpanzees and humans (Breuer, Ndoundou-Hockemba et al. 2005). Brain plasticity that occurs during tool usage was demonstrated by the experiments in which monkeys used a rake to retrieve distant objects (Iriki, Tanaka et al. 1996). As the monkey practiced, cortical neurons extended their visual receptive fields along the length of the rake. Remapping of the “body schema” during tool usage has been also demonstrated in psychophysics experiments in humans (Gurfinkel, Levick et al. 1991; Maravita, Spence et al. 2003). Additionally, in a neuroimaging study (Maruishi, Tanaka et al. 2004) specific activations of the right ventral premotor cortex occurred during manipulation of a myoelectric prosthetic hand. These results strongly suggest that long-term usage of an actuator, which is directly controlled by brain activity, may evoke substantial cortical and subcortical remapping. Likely, during this process of remapping a new vivid perceptual experience will emerge: the subjects will start to perceive the artificial actuator as if it belongs to the subject’s own body. In support of this suggestion, primary sensorimotor cortex activation is observed during perceived voluntary movements of phantom limbs in amputees (Roux, Lotterie et al. 2003).

Recently, a series of remarkable demonstrations of tool assimilation was obtained when experimental animals learned to operate an actuator through BMIs. At the peak of their performance, these animals were capable of operating such interfaces without the need of moving their own limbs (Chapin, Moxon et al. 1999; Taylor, Tillery et al. 2002; Carmena, Lebedev et al. 2003; Lebedev, Carmena et al. 2005).

Even if decoding algorithms were initially trained to predict overt movements of the animals’ limbs, after animals started to control the actuator through the BMI and stopped moving their own limbs, they still continued to control the actuator through modulations of their neurons’ firing rate. Our analysis showed that, after the mode of operation was switched to direct BMI control, even when the animals still continued to move their own arms, neuronal tuning of the movements of the subject’s own limbs decreased (Lebedev, Carmena et al. 2005). This finding indicated that the neuronal activations became dedicated to controlling the actuator movements. One interpretation of this result is that the brain gradually assimilated the actuator within the same maps that represented the animal’s own body (Carmena, Lebedev et al. 2003; Lebedev, Carmena et al. 2005). In agreement with this hypothesis, the several studies of continuous BMI operations in primates have now reported physiological changes in neuronal tuning, which include changes in a neuron’s preferred direction and direction tuning strength (Taylor, Tillery et al. 2002; Carmena, Lebedev et al. 2003; Lebedev, Carmena et al. 2005). In addition, broad changes in pairwise neuronal correlation occur under brain-control mode (Carmena, Lebedev et al. 2003; Lebedev, Carmena et al. 2005). Although neuronal plasticity associated with BMI usage needs to be studied in much more detail, it is clear that it can underlie incorporation of neural prostheses into the internal representation of the body during prolonged BMI operations in human users.

SOMATOSENSORY FEEDBACK IN BMI DESIGN

Normally, the sense of ownership of the limb is facilitated by the continuous peripheral tactile and proprioceptive signals that occur when the limb moves and interacts with external objects (Gurfinkel, Levick et al. 1991; Maravita, Spence et al. 2003).

Looking into the future, we envision that limb prostheses will be equipped with a variety of sensors providing multiple channels of artificial somatosensory feedback signals, which can better inform the subject’s brain about the continuous functions of these actuators. Such sensory feedback will be different from the one provided in current BMI designs, in which animals receive sensory information from the actuator through visual feedback (Serruya, Hatsopoulos et al. 2002; Taylor, Tillery et al. 2002; Carmena, Lebedev et al. 2003; Lebedev, Carmena et al. 2005). Until recently, the use of tactile and proprioceptive feedback in BMI research had remained untouched. During the last two years, we have started to investigate the possibility of providing sensory feedback information from a robotic actuator to the brain using multichannel micro-stimulation of cortical somatosensory areas. The idea of providing sensory feedback using microstimulation is supported by previous studies that demonstrated the ability of monkeys in sensing microstimulation patterns and using this information to guide their behavioral responses (Romo, Hernandez et al. 2000; Cohen and Newsome 2004). In our laboratory, we carried out a long-term study in which owl monkeys learned to guide their reaching movements by decoding vibratory stimuli applied to their arms (Fitzsimmons, Drake et al. 2007; see chapter 7 in this volume). In the following study, instead of vibratory stimulation, patterns of microstimulation pulses were applied through the electrodes implanted in the primary somatosensory cortex (see chapter 3 in this volume). The same monkeys were still able to correctly interpret the instructions provided by cortical microstimulation. Moreover, the monkeys’ performance guided by microstimulation was so good that they eventually surpassed the level of performance observed in the experiments with vibrostimulation applied to the skin. These results suggest that cortical microstimulation and other sensory inputs to the brain may become useful to “sensorize” prosthetic limbs controlled by a BMI.

CONCLUSION

We envision that future neuroprosthesis will include a fully implantable recording system that wirelessly transmits multiple neuronal ensemble signals to a BMI module that decodes motor commands and cognitive characteristics of the action the subject intends to perform. Such BMI will analyze both high-order commands, derived from the brain activity, and peripheral feedback signals involved in artificial reflex-like control loops. This shared-control mode of operation will ensure high accuracy of movements performed by a multiple-degree-of-freedom prosthetic device or by the subject’s own limbs activated by functional electrical stimulation of peripheral nerves and muscles. Moreover, arrays of touch and position sensors will produce multiple feedback signals that could be delivered to brain somatosensory areas using multichannel microstimulation. We hope that such hybrid BMIs may one day restore motor functions in patients suffering from devastating consequences of multiple neurological disorders.

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