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SYMPOSIUM REPORT |
1 Department of Physiology and Biophysics, Washington National Primate Research Center, University of Washington, Seattle, WA 98195-7290, USA
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Abstract |
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(Received 20 December 2006;
accepted after revision 11 January 2007;
first published online 18 January 2007)
Corresponding author E. E. Fetz: Department of Physiology and Biophysics, Washington National Primate Research Center, University of Washington, Seattle, WA 98195-7290, USA. Email: fetz{at}u.washington.edu
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Introduction |
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TopAbstractIntroductionReferences |
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Volitional activation associated with behaviour
The most obvious place to find cortical signals directly associated with volitional movements is primary motor cortex, where activity of accessible neurons is closely correlated with voluntary limb movement. Innumerable studies have demonstrated that cells in motor cortex and various premotor areas discharge with execution of voluntary movements in relatively specific and reliable ways. The diverse range of limb movements and the flexibility of digital control must clearly be correlated with correspondingly flexible activation of cortical cells that generate these movements. Relationships to movements can also be seen in cortical regions beyond traditional motor areas. In primary somatosensory cortex many cells that exhibit classic sensory responses to peripheral stimulation also fire prior to active movements, much like precentral motor cortex cells (Soso & Fetz, 1980); over half of the postcentral cells began discharging prior to activation of agonist muscles, revealing the existence of a central volitional drive that is superimposed on their peripheral input. Multiunit recordings in diverse cortical areas reveal that the parameters of free limb movements can be predicted from the activity of neurons in different pre- and postcentral cortical areas, with varying degrees of accuracy (Wessberg et al. 2000; Carmena et al. 2003).
Neurons in motor areas often fire also with imagined movements in the absence of execution. PET and fMRI studies have shown that many cortical areas associated with generating volitional movement are also activated when the subject simply imagines making the movement (Jeannerod, 1995; Roth et al. 1996; Jeannerod & Frak, 1999; Niyazov et al. 2005). Motor imagery is also effective in modulating synchrony and power in the EEG and ECoG (Pfurtscheller & Neuper, 1997; McFarland et al. 2000; Pfurtscheller et al. 2000; Leuthardt et al. 2004). Activation with motor imagery is further demonstrated by the decreased thresholds for evoking movements with transcranial magnetic stimulation (Kasai et al. 1997; Fadiga et al. 1999; Stinear & Byblow, 2003; Niyazov et al. 2005; Fourkas et al. 2006).
In addition to real or imagined movements, many cortical cells are modulated with movement preparation. This has been amply documented in studies that involve an instructed delay period, in which cortical cells may modulate their activity during the interval between the instructional cue and the ‘go’ signal (Wise et al. 1983; Kurata & Wise, 1988; Alexander & Crutcher, 1990; Riehle & Requin, 1995; Crutcher et al. 2004). Occurring after the end of any sensory response to the cue and well before the onset of the triggered movement, this instructed delay period activity may code information about the cue or preparation to move, but in either case reflects a volitionally generated activity. Neural activity associated with specific motor planning has been demonstrated in posterior parietal areas and may provide useful signals for decoding intended movements (Snyder et al. 2000; Shenoy et al. 2003; Musallam et al. 2004; Santhanam et al. 2006).
Neurons in sensory association areas are also volitionally activated in conjunction with cognitive imagery. In the temporal lobe many single neurons that respond selectively to a particular visual stimulus are in addition specifically activated during imaginative recall of the same effective stimulus (Kreiman et al. 2000). Thus, internal representations of stimuli and movements often employ many of the same neurons involved in overt sensory or motor behaviour. Beyond representations of sensory and motor events, internal cognitive activity like ‘thinking’ must also have neural correlates and these also represent volitionally controllable processes. These neural activities are independent of sensory input or motor output, and indeed operate autonomously because they are effectively buffered from peripheral activity.
Recent fMRI studies have shown that volitional shifts in attention activate widespread cortical areas in the absence of any sensory or motor correlates (Kastner et al. 1999). When subjects are fixating on a target spot and are cued to shift their attention to another part of the visual field, anterior cortical sites exhibit strong increases in activation, almost as large as the responses to an overt visual stimulus. Even primary visual cortex shows the effect of volitional shifts of attention, in the absence of any visual stimulus.
Thus, conventional experiments have revealed a range of circumstances in which central control of neural activity is evident. Volitional input could be considered to reflect an activating modality existing in addition to the better-studied sensory and motor modalities. The degree to which it is available for BCI/BMI control signals remains to be empirically determined. Conventional experiments, such as those described, are typically designed around a particular behaviour, and indirectly reveal the volitional components of correlated neural activity. Reversing this paradigm, biofeedback experiments directly elicit the volitional control of neural activity and allow the correlated behaviour to emerge.
Volitional activation revealed by biofeedback
The volitional drive on cortical neurons can be demonstrated directly by operantly training subjects to control the activity of neural activity with biofeedback. For example, operant conditioning experiments showed that monkeys were able to quickly increase and decrease the activity of motor cortex cells when rewarded for these changes (Fetz, 1969; Fetz & Baker, 1973). The degree to which cell activity met the criterion for reward was continuously represented in the displacement of a meter arm, whose rightward position corresponded to the threshold for the feeder discharge. Once the monkeys had discriminated this feedback they were able to drive the meter arm with newly isolated units and could modify their control strategy within minutes as the reward criteria were changed. Figure 1 shows an example of differential control of two neighbouring motor cortex cells. The firing rate of the unit with the larger action potential could be increased independently of the rate of the ‘smaller’ unit, and vice versa. Moreover, the monkey could also decrease the rate of the large unit (after several minutes of attempting increases, which had been previously rewarded). This bidirectional volitional control eliminates explanations involving non-specific effects like arousal or reward expectancy. Interestingly, these two units both responded reliably to passive extension of the knee, showing again that the central volitional drive on cells is controllable independently of peripheral input.
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These studies are representative of a large body of experiments that have investigated the direct control of neural activity in the CNS through biofeedback (Barber et al. 1971–1977; Chase, 1974; Birbaumer & Kimmel, 1979). Given explicit visual feedback, subjects could volitionally control a number of physiological parameters that would otherwise remain unconscious. Volitional control of the activity of single neurons was initially investigated with single motoneurons through biofeedback of single motor unit activity (Harrison & Mortensen, 1962; Basmajian, 1963). Biofeedback worked well for activating low-threshold motor units in isolation, but not high threshold units; attempts to reverse recruitment order of motor units largely failed to demonstrate violations of the size principle. Olds pioneered CNS unit conditioning studies by operantly rewarding rats to increase the activity of midbrain neurons using intracranial stimulation (Olds, 1965). Biofeedback control of autonomic activity was also explored extensively, as described in Barber et al. (1971–1977) and Birbaumer & Cohen (2007).
Figure 2 illustrates the basic components of biofeedback experiments. The defining feature is the feedback about the state of the controlled variable made explicitly available to the ‘volitional controller’ – namely, the rest of the brain. The brain in turn uses the feedback to modify the controlled variable. In animal experiments additional feedback is often provided by rewarding the appropriate changes. An important concomitant of the reinforced activity is the correlated activity, which may have a causal relationship with the controlled variable or may be only adventitiously associated. For example, in biofeedback conditioning of single motor cortex cell activity, the correlated responses included the causally related activation of those cells directly driving the reinforced neuron, as well as associated motor activity that could be adventitiously related to the cell activity and be dissociable. Similarly, motor activity could affect many different conditioned variables – for example absence of movement enhances the precentral mu or beta rhythm (Pfurtscheller, 1981), motor activity is associated with hippocampal theta rhythms (Black, 1972), and closing the eyes enhances the appearance of occipital alpha activity (Mulholland & Eberlin, 1977; Ancoli & Kamiya, 1978). In many clinical applications of biofeedback the point of controlling the feedback variable (e.g. scalp temperature) was to change the correlated variable (blood flow and associated migraine headaches).
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Volitional activation revealed by BCI and BMI studies
The volitional control of cortical cell activity has now been dramatically demonstrated in numerous BCI and BMI studies in which primates controlled the position of cursors or robotic arms with cortical activity under closed-loop conditions (Serruya et al. 2002; Taylor et al. 2002; Carmena et al. 2003). Under ‘open-loop’ conditions, the activity of neural populations could be linearly transformed to the 3-D coordinates of the monkeys' hand as they retrieved food from a well and brought it to their mouth (Wessberg et al. 2000). Interestingly, the conversion parameters obtained for one set of trials provided increasingly poor predictions of future responses, indicating a source of drift over tens of minutes in the open-loop condition. This problem was alleviated when the monkeys observed the consequences of their neural activity in ‘real time’ and could optimize cell activity to achieve the desired goal under ‘closed-loop’ conditions. For example, monkeys could successfully acquire targets on a two-dimensional workspace (Serruya et al. 2002) or in virtual 3-D space (Taylor et al. 2002) with a cursor driven by activity of 10–30 motor cortex neurons. More recently, the weighted activity of cell ensembles recorded over many cortical areas was used to control a robotic arm to reach and grasp objects (Carmena et al. 2003). Significantly, several of these studies also demonstrated the ability to extract movement predictions from neurons in postcentral as well as precentral cortical areas (Wessberg et al. 2000; Carmena et al. 2003) (Fig. 3). Precentral motor cortex cells provided the most accurate predictions of force and displacement, but neurons from many other areas also provided significant predictions. The prediction accuracy increased with the number of cells included, albeit with diminishing returns.
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The basic BCI/BMI paradigm (Fig. 4) is essentially identical to the biofeedback paradigm. One emphasized difference is the transform algorithm converting neural activity to the control parameters needed to operate the device. This interposes an intermediate stage that may complicate the relationship between neural activity and the final output control of the device. The explicit reward loop has been eliminated to suggest that the volitional controller is typically motivated to operate the controlled device, although many animal experiments also employ a reward.
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A comparable and related flexibility is demonstrated by the neural mechanisms that buffer mental activity from sensory input and motor output. Mental activity must be shielded from sensory disruption in order to operate independently of environmental events. It must also be dissociated from motor output to prevent imagined activity from being acted out and allow thinking to occur independently of movements. Yet these internal representations often employ many of the same neurons involved in overt sensory or motor behaviour. A highly flexible buffering component of mental operations allows central mechanisms to quickly switch between accessing sensory information or generating appropriate movements and performing the internal processing independently. These flexible switching operations are evident in BMI studies that tap the central activity and link it directly to external devices.
Limitations on control for BCI and BMI
Given the degree to which independent control of cortical units can be rapidly acquired in biofeedback experiments (e.g. Fig. 1), one might wonder why the control of BCIs and BMIs through neural activity is not more accurate than it is. Without minimizing the remarkable achievements of these studies, one can ask whether the limitations in accurate control are inherent or could be further addressed. There could be several possible explanations for these limitations. First, the complex transforms of neural activity to output parameters may complicate the degree to which neural control can be learned. In contrast to the relatively simple task of driving one or two cells in bursts while allowing free performance of any correlated responses, the requirement to modulate activity of a population to accurately control a transformed function may be more difficult because the effect of any particular cell is largely submerged in the population function. Moreover, activity of each cell in the population has some stochastic component which may conspire against learning optimal control of any particular cell (Carmena et al. 2005).
Second, the degree of independent control of cells may be inherently constrained by ensemble interactions. A special example of such a constraint is the fixed relative recruitment order of motoneurons according to the size principle, which has foiled attempts to activate high threshold motor units independently of lower threshold units. Neural ensembles may have comparable limits on the degree to which individual elements can be independently activated. To the extent that internal representations depend on relationships between the activities of neurons in an ensemble, the processing of these representations involves corresponding constraints on the independence of those activities. These constraints may explain the diminishing returns obtained from increasing the number of neurons included in a linear filter (Carmena et al. 2003). The ‘neuron dropping curves’ representing the average accuracy as a function of the number of cells have extrapolated asymptotes below 100% for indefinitely large populations (Fig. 3). Yet, it remains possible that longer experience with the same neuronal ensembles could improve the achievable accuracy.
A third source of difficulty in achieving reliable control may come from employing adaptive decoding schemes. Although such adaptive algorithms are intended to automatically optimize control, they create a moving target for volitional modulation; the neural activity pattern that worked at one time may subsequently become less effective, requiring the learning of new patterns.
Finally, the ability to learn optimal control may be limited by the short and intermittent exposure times, dictated by the need to tether the subject to the requisite instrumentation. For example, a paraplegic subject that could practise neural control of a cursor only several hours a week demonstrated remarkable success in controlling a cursor movement, but nevertheless achieved a limited degree of accuracy (Hochberg et al. 2006). Intermittent sessions also involve possible changes in the recorded neuronal population, requiring the subject to relearn the task with a slightly different population of cells. These factors suggest that the range and reliability of neural control in BMI might increase significantly when prolonged stable recordings are achieved and the subject can practise under consistent conditions over extended periods of time. This would involve implantable circuitry that can monitor the same neural activity over many days.
Implantable recurrent brain–computer interfaces
Recognizing the need for implantable circuitry for further improvement in BMI control, many laboratories are developing compact, low-power integrated circuits (Mojarradi et al. 2003; Obeid et al. 2004; Berger & Glanzmann, 2005; Mohseni et al. 2005). For example, we have investigated the operation of a small computer chip in conjunction with wire electrodes implanted in monkey motor cortex (Mavoori et al. 2005). This ‘Neurochip’ reliably recorded the activity of the same single neurons and two related arm muscles for weeks, storing raw and/or compressed data to memory for daily downloading via an infrared link (Jackson et al. 2007). The compact connections and self-contained circuitry makes unit recordings remarkably stable despite the unconstrained movements of the monkey in the cage. For many neurons the correlations between neural and muscle activity remained relatively stable, which bodes well for prosthetic applications.
The Neurochip can also operate in a recurrent loop mode, converting action potentials of a cortical neuron to stimuli delivered elsewhere in the motor system. Thus the cortical cell could directly control functional electrical stimulation of muscles, spinal cord or other brain regions (Jackson et al. 2006b). Continuous operation of such a recurrent BCI (R-BCI) should allow the subject to adapt to the artificial pathway and by appropriately modifying the neural activity, to incorporate its operation into normal behaviour. Such a R-BCI has obvious potential prosthetic applications in bridging lost biological connections, particularly when multiple parallel channels are implemented.
A second therapeutic potential is the possible strengthening of weak or impaired physiological connections. When the R-BCI was configured to connect neighbouring motor cortex sites, action potentials recorded at one site triggered synchronous stimulation at the second site (Jackson et al. 2006a). Continuous operation for a day or more of normal behaviour resulted in long-term changes in the output effects evoked from the recording site (Fig. 5). Surprisingly, these changes remained stable for over a week of testing after the conditioning paradigm had terminated. Such conditioning effects were not simply due to the stimulation alone, but involved time-dependent plasticity: testing numerous pairs of sites in this paradigm showed that none of the control sites exhibited any changes, and the effect was obtained only when the delays between spikes and stimuli were less than 50 ms.
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Footnotes |
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References |
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TopAbstractIntroductionReferences |
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Ancoli S & Kamiya J (1978). Methodological issues in alpha biofeedback training. Biofeedback Self Regul 3, 159–183.[CrossRef][Medline]
Barber TX, Dicara LV, Kamiya J, Miller NE, Shapiro D & Stoyva J (1971–77). Biofeedback and Self-Control. Aldine-Atherton, Chicago.
Basmajian JV (1963). Control and training of of individual motor units. Science 141, 440–441.
Berger TW, Ahuja A, Courellis SH, Deadwyler SA, Erinjippurath G, Gerhardt GA, Gholmieh G, Granacki JJ, Hampson R, Hsaio MC, LaCoss J, Marmarelis VZ, Nasiatka P, Srinivasan V, Song D, Tanguay AR & Wills J (2005). Restoring lost cognitive function. IEEE Eng Med Biol Mag 24, 30–44.[Medline]
Berger TW & Glanzmann DL (2005). Toward Replacement Parts for the Brain. MIT Press, Cambridge.
Birbaumer N & Cohen LG (2007). Brain–computer interfaces (BCI): communication and restoration of movement in paralysis. J Physiol 579, 621–636.
Birbaumer N & Kimmel H (1979). Biofeedback and Self-Regulation. Erlbaum, Hillsdale.
Black AH (1972). The operant conditioning of central nervous system electrical activity. In The Psychology of Learning and Motivation, ed. Bower GH, pp. 47–95. Academic Press, New York.
Carmena JM, Lebedev MA, Crist RE, O'Doherty JE, Santucci DM, Dimitrov DF, Patil PG, Henriquez CS & Nicolelis MA (2003). Learning to control a brain–machine interface for reaching and grasping by primates. PLoS Biol 1, E42.[Medline]
Carmena JM, Lebedev MA, Henriquez CS & Nicolelis MA (2005). Stable ensemble performance with single-neuron variability during reaching movements in primates. J Neurosci 25, 10712–10716.
Chapin JK, Moxon KA, Markowitz RS & Nicolelis MA (1999). Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex. Nat Neurosci 2, 664–670.[CrossRef][Medline]
Chase MH (1974). Operant Control of Brain Activity, vol. 2. Brain Information Service/Brain Research Institute, UCLA, Los Angeles.
Crutcher MD, Russo GS, Ye S & Backus DA (2004). Target-, limb-, and context-dependent neural activity in the cingulate and supplementary motor areas of the monkey. Exp Brain Res 158, 278–288.[Medline]
Donoghue JP, Nurmikko A, Black M & Hochberg LR (2007). Assistive technology and robotic control using MI ensemble-based neural interface systems in humans with tetraplegia. J Physiol 579, 603–611.
Fadiga L, Buccino G, Craighero L, Fogassi L, Gallese V & Pavesi G (1999). Corticospinal excitability is specifically modulated by motor imagery: a magnetic stimulation study. Neuropsychologia 37, 147–158.[CrossRef][Medline]
Fetz EE (1969). Operant conditioning of cortical unit activity. Science 163, 955–958.
Fetz EE & Baker MA (1973). Operantly conditioned patterns on precentral unit activity and correlated responses in adjacent cells and contralateral muscles. J Neurophysiol 36, 179–204.
Fetz EE & Finocchio DV (1975). Correlations between activity of motor cortex cells and arm muscles during operantly conditioned response patterns. Exp Brain Res 23, 217–240.[Medline]
Fourkas AD, Ionta S & Aglioti SM (2006). Influence of imagined posture and imagery modality on corticospinal excitability. Behav Brain Res 168, 190–196.[CrossRef][Medline]
Harrison VF & Mortensen OA (1962). Identification and voluntary control of single motor unit activity in the tibialis anterior muscle. Anat Rec 144, 109–116.[CrossRef][Medline]
Hochberg LR, Serruya MD, Friehs GM, Mukand JA, Saleh M, Caplan AH, Branner A, Chen D, Penn RD & Donoghue JP (2006). Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 442, 164–171.
Jackson A, Mavoori J & Fetz EE (2006a). Long-term motor cortex plasticity induced by an electronic neural implant. Nature 444, 56–60.
Jackson A, Mavoori J & Fetz EE (2007). Correlations between the same motor cortex cells and arm muscles during a trained task, free behavior and natural sleep in the macaque monkey. J Neurophysiol 97, 360–374.
Jackson A, Moritz CT, Mavoori J, Lucas TH & Fetz EE (2006b). The Neurochip BCI: towards a neural prosthesis for upper limb function. IEEE Trans Neural Syst Rehabil Eng 14, 187–190.[CrossRef][Medline]
Jeannerod M (1995). Mental imagery in the motor context. Neuropsychologia 33, 1419–1432.[CrossRef][Medline]
Jeannerod M & Frak V (1999). Mental imaging of motor activity in humans. Curr Opin Neurobiol 9, 735–739.[CrossRef][Medline]
Kasai T, Kawai S, Kawanishi M & Yahagi S (1997). Evidence for facilitation of motor evoked potentials (MEPs) induced by motor imagery. Brain Res 744, 147–150.[CrossRef][Medline]
Kastner S, Pinsk MA, De Weerd P, Desimone R & Ungerleider LG (1999). Increased activity in human visual cortex during directed attention in the absence of visual stimulation. Neuron 22, 751–761.[CrossRef][Medline]
Kennedy PR, Bakay RA, Moore MM, Adams K & Goldwaithe J (2000). Direct control of a computer from the human central nervous system. IEEE Trans Rehabil Eng 8, 198–202.[CrossRef][Medline]
Kreiman G, Koch C & Fried I (2000). Imagery neurons in the human brain. Nature 408, 357–361.
Kurata K & Wise SP (1988). Premotor and supplementary motor cortex in rhesus monkeys: neuronal activity during externally- and internally-instructed motor tasks. Exp Brain Res 72, 237–248.[Medline]
Leuthardt EC, Schalk G, Wolpaw JR, Ojemann JG & Moran DW (2004). A brain–computer interface using electrocorticographic signals in humans. J Neural Eng 1, 63–71.[CrossRef][Medline]
McFarland DJ, Miner LA, Vaughan TM & Wolpaw JR (2000). Mu and beta rhythm topographies during motor imagery and actual movements. Brain Topogr 12, 177–186.[CrossRef][Medline]
Mavoori J, Jackson A, Diorio C & Fetz E (2005). An autonomous implantable computer for neural recording and stimulation in unrestrained primates. J Neurosci Meth 148, 71–77.[CrossRef][Medline]
Mohseni P, Najafi K, Eliades SJ & Wang X (2005). Wireless multichannel biopotential recording using an integrated FM telemetry circuit. IEEE Trans Neural Syst Rehabil Eng 13, 263–271.[CrossRef][Medline]
Mojarradi M, Binkley D, Blalock B, Andersen R, Ulshoefer N, Johnson T & Del Castillo L (2003). A miniaturized neuroprosthesis suitable for implantation into the brain. IEEE Trans Neural Syst Rehabil Eng 11, 38–42.[CrossRef][Medline]
Mulholland T & Eberlin P (1977). Effect of feedback contingencies on the control of occipital alpha. Biofeedback Self Regul 2, 43–57.[CrossRef][Medline]
Musallam S, Corneil BD, Greger B, Scherberger H & Andersen RA (2004). Cognitive control signals for neural prosthetics. Science 305, 258–262.
Niyazov DM, Butler AJ, Kadah YM, Epstein CM & Hu XP (2005). Functional magnetic resonance imaging and transcranial magnetic stimulation: effects of motor imagery, movement and coil orientation. Clin Neurophysiol 116, 1601–1610.[CrossRef][Medline]
Obeid I, Nicolelis MA & Wolf PD (2004). A low power multichannel analog front end for portable neural signal recordings. J Neurosci Meth 133, 27–32.[CrossRef][Medline]
Olds J (1965). Operant conditioning of single unit responses. Excerpta Med Int Cong Series 87, 372–380.
Pfurtscheller G (1981). Central beta rhythm during sensorimotor activities in man. Electroencephalogr Clin Neurophysiol 51, 253–264.[CrossRef][Medline]
Pfurtscheller G & Neuper C (1997). Motor imagery activates primary sensorimotor area in humans. Neurosci Lett 239, 65–68.[CrossRef][Medline]
Pfurtscheller G, Neuper C & Krausz G (2000). Functional dissociation of lower and upper frequency mu rhythms in relation to voluntary limb movement. Clin Neurophysiol 111, 1873–1879.[CrossRef][Medline]
Riehle A & Requin J (1995). Neuronal correlates of the specification of movement direction and force in four cortical areas of the monkey. Behav Brain Res 70, 1–13.[CrossRef][Medline]
Roth M, Decety J, Raybaudi M, Massarelli R, Delon-Martin C, Segebarth C, Morand S, Gemignani A, Decorps M & Jeannerod M (1996). Possible involvement of primary motor cortex in mentally simulated movement: a functional magnetic resonance imaging study. Neuroreport 7, 1280–1284.[Medline]
Santhanam G, Ryu SI, Yu BM, Afshar A & Shenoy KV (2006). A high-performance brain–computer interface. Nature 442, 195–198.
Serruya MD, Hatsopoulos NG, Paninski L, Fellows MR & Donoghue JP (2002). Instant neural control of a movement signal. Nature 416, 141–142.
Shenoy KV, Meeker D, Cao S, Kureshi SA, Pesaran B, Buneo CA, Batista AP, Mitra PP, Burdick JW & Andersen RA (2003). Neural prosthetic control signals from plan activity. Neuroreport 14, 591–596.[CrossRef][Medline]
Snyder LH, Batista AP & Andersen RA (2000). Intentionrelated activity in the posterior parietal cortex: a review. Vision Res 40, 1433–1441.[CrossRef][Medline]
Soso MJ & Fetz EE (1980). Responses of identified cells in postcentral cortex of awake monkeys during comparable active and passive joint movements. J Neurophysiol 43, 1090–1110.
Stinear CM & Byblow WD (2003). Motor imagery of phasic thumb abduction temporally and spatially modulates corticospinal excitability. Clin Neurophysiol 114, 909–914.[CrossRef][Medline]
Taylor DM, Tillery SI & Schwartz AB (2002). Direct cortical control of 3D neuroprosthetic devices. Science 296, 1829–1832.
Wessberg J, Stambaugh CR, Kralik JD, Beck PD, Laubach M, Chapin JK, Kim J, Biggs SJ, Srinivasan MA & Nicolelis MA (2000). Real-time prediction of hand trajectory by ensembles of cortical neurons in primates. Nature 408, 361–365.
Wise SP, Weinrich M & Mauritz KH (1983). Motor aspects of cue-related neuronal activity in premotor cortex of the rhesus monkey. Brain Res 260, 301–305.[CrossRef][Medline]
Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G & Vaughan TM (2002). Brain–computer interfaces for communication and control. Clin Neurophysiol 113, 767–791.[CrossRef][Medline]
Wolpaw JR & McFarland DJ (2004). Control of a two-dimensional movement signal by a noninvasive brain–computer interface in humans. Proc Natl Acad Sci U S A 101, 17849–17854.
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’ and ‘
’ indicating whether activity of the unit drove the biofeedback meter arm towards or away from level for triggering feeder. During ‘S
’ (time-out) periods feedback meter and feeder were turned off. (Used with permission from 
