Computer cursor control by motor cortical signals in humans with tetraplegia

Transcription

1 Computer cursor control by motor cortcal sgnals n humans wth tetraplega Sung-Phl Km, John D. Smeral, Legh R. Hochberg, John P. Donoghue and Mchael J. Black Abstract A drect neural nterface system (NIS) promses to provde communcaton and ndependence to persons wth paralyss by harnessng ntact motor cortcal sgnals to enable controllng prosthetc devces. An ntracortcal NIS ams to acheve ths by sensng extracellular neuronal sgnals through chroncally mplanted mcroelectrodes and by decodng the spkng actvty of neurons nto prosthetc control sgnals. In non-human prmate studes, decodng has been performed by fndng a relatonshp between neuronal sgnals and actual lmb movements. However, such decodng approaches face challenges n the case of paralyzed persons where there s no true movement nformaton. Specfcally, we have focused on dealng wth several key questons n decodng of neural actvty n humans wth paralyss: what movement parameters should be decoded?; whch decodng algorthms lead to more accurate estmaton of movement parameters?; how do we tran decodng algorthms wthout observng actual movement parameters?; and how many control parameters can be decoded from a sngle neural ensemble? In ths paper, we summarze our recent studes to address these questons to mprove decodng performance, whch enables a human wth tetraplega to drve a 2D computer cursor to an arbtrary poston and execute a clck on the area of nterest. I. INTRODUCTION neural nterface system (NIS) promses to restore some A lost functon to persons wth paralyss who suffer from locked-n syndrome, amng to provde communcaton and Manuscrpt receved January 31, Ths work was supported n part by MGH-Deane Insttute, NIH/NINDS-Javts (NS-25074)/NICHD-NCMRR (N01-HD-53403)/NIBIB, Dept. of Veterans Affars, Rehabltaton R&D Servce, ONR (N ), Cyberknetcs Neurotechnology Systems, Inc., Dors Duke Chartable Foundaton, Kate Samson Foundaton, Natonal Center for Research Resources (NCRR C A1). Dsclosures: LRH: Clncal tral support, Cyberknetcs Neurotechnology Systems, Inc. (CYKN); JDS: Consultant, CYKN; JPD: pror Chef Scentfc Offcer and drector, stock holdngs, CYKN. S.-P. Km and M. J. Black are wth the Dept. of Computer Scence, Brown Unv., Provdence, RI, USA (phone: ; fax: ; e-mal: (spkm@ cs.brown.edu). J. D. Smeral s wth the Center for Restoratve and Regeneratve Medcne, Rehabltaton R&D Servce, Dept. of Veterans Affars Medcal Center, Provdence RI, USA; and the Dept. of Neuroscence, Brown Unv., Provdence, RI, USA (e-mal: John_Smeral@brown.edu). L. R. Hochberg s wth the Center for Restoratve and Regeneratve Medcne, Rehabltaton R&D Servce, Dept. of Veterans Affars Medcal Center, Provdence RI, USA; Dept. of Neuroscence, Brown Unv., Provdence, RI, USA; and Massachusetts General Hosptal, Harvard Medcal School, Boston, MA, USA (e-mal: Legh_Hochberg@brown.edu). J. P. Donoghue s wth the Dept. of Neuroscence, Brown Unv., Provdence, RI, USA and Cyberknetcs Neurotechnology Systems, Inc., Foxborough, MA, USA (e-mal: John_Donoghue@brown.edu). ndependence. The fundamental concept n an NIS s to connect the ntact cortcal sgnals of a person wth paralyss drectly to external prosthetc devces, bypassng damaged, dseased or mssng structures. An ntracortcal NIS ams to acheve ths by sensng extracellular neural sgnals n the motor cortex usng chroncally mplanted mcroelectrodes and translatng those sgnals nto motor control parameters to actuate prosthetc devces. A plot clncal study for feasblty of a human NIS began n 2004 (Investgatonal Devce Exempton). The frst report from ths study by Hochberg et al. [1] demonstrated that an ntracortcal NIS could detect movement-related sgnals from the motor cortex of a person wth spnal cord njury years after njury and that these sgnals could be used for voluntary control of external devces, ncludng a robotc hand, a computer cursor and other physcal and vrtual devces. In partcular, computer cursor control has been a prmary applcaton snce t provdes a foundaton for a varety of drect communcaton tools. Central to ths study and underlyng the effectve functonng of the NIS, s the understandng of the codng of movement-related nformaton n the actvty of cortcal neurons. Spkes (or acton potentals) are known to be an mportant nformaton sgnal of neurons and spke actvty can be closely related to movement parameters, such as movement speed or drecton. A number of studes n non-human prmates have explored how neural spkng actvty s modulated when plannng, executng and adjustng lmb control. However, neural control for prosthetc devces wthout knowng true lmb movements rases a new queston: how can we decode the observed neural spkng actvty only wth the unobservable ntenton of movement? Ths queston leads to several ssues n the development of an NIS for paralyzed persons: Whch knematc parameters n the magned movement are most naturally represented n neuronal ensemble actvty? Whch mathematcal algorthms should we use to decode neural spkng actvty? How can we generate tranng samples only wth magned movements to optmze the parameters of decodng algorthms? How many control varables can we extract from a sngle neuronal ensemble? In ths paper, we summarze how we addressed some of these ssues and what we have found from the study of decodng neural actvty n humans wth tetraplega for prosthetc control. Prevous non-human prmate studes have shown that motor cortcal neurons encode varous movement parameters,

2 ncludng hand poston, hand velocty, lmb force and jont torques [2-9]. We have studed how some of these movement parameters were correlated wth neural actvty when a person magned cursor movement. We have found that cursor velocty was more correlated wth neural actvty than other parameters such as poston and acceleraton n two partcpants [10]. A number of mathematcal algorthms have been developed to decode movement parameters from neural actvty (see [11-12] for revew). In partcular, some studes suggested that the decodng algorthms based on probablstc Bayesan nference estmated arm/hand knematc parameters better than smple drect estmators such as lnear flters [13-16]. Accordngly, we used the Kalman flter [13-14], to decode the movement of a computer cursor from neural actvty n humans wth tetraplega. We have demonstrated that the Kalman flter mproved decodng of cursor velocty when compared wth a lnear flterng method [10]. In non-human able-boded prmate studes, decodng algorthms can be traned to assocate neural actvty wth lmb movements usng tranng samples generated by smultaneously recordng both movement and neural sgnals. In the study of humans wth paralyss, however, tranng of a decodng algorthm must be acheved n the absence of physcal movement. In Hochberg et al. [1], a combnaton of open-loop and closed-loop tranng methods was used that enabled subjects to gan control of a neural cursor. In ths tranng approach, a subject vewed a cursor movng on a montor and was nstructed to magne movng the cursor. Then the cursor movement knematcs and synchronzed neural sgnals were used to tran a decodng algorthm. We modfed ths procedure by addng a new tranng task for velocty-based decodng algorthms, emphaszng velocty n the desgn of cursor moton [10]. We also extended t to generate new tranng data that were used for tranng a mult-state decoder [19]. A practcal neural cursor nterface should provde the utlty of a computer mouse. Ths combnes the ablty to move a cursor to an arbtrary screen locaton, hold the cursor stll, and execute a clck acton to make a selecton at that locaton. A central queston to pont-and-clck cursor control s whether mult-state sgnals (.e. both a contnuous state for pontng and a dscrete state for clckng) can be smultaneously decoded from a sngle neuronal ensemble. Based on non-human prmate studes that demonstrated mult-state sgnals could be decoded from the motor cortcal actvty of monkeys [17-18], we have modfed a prevous probablstc decodng model [18] to make t feasble for real-tme performance. Our recent study has shown that we could successfully decode both contnuous and dscrete states from a sngle neuronal ensemble n a human [19]. We have demonstrated n ths study that a human wth tetraplega could use the NIS wth the mult-state decoder to pont to and clck on targets. In the remander of ths paper, we present more detals of the ssues and fndngs n decodng neural actvty of humans wth paralyss. In the secton III, we llustrate tranng methods for the decodng algorthms. In secton IV, we descrbe the correlaton between neural actvty and magned cursor movements. In the secton V and VI, we revew the decodng algorthms and demonstrate closed-loop neural cursor control performance usng each of the decodng algorthms. In the secton VII, we descrbe how we enabled pont-and-clck cursor control. In the fnal secton, we summarze on-gong challenges and how we wll address them. II. NEURAL INTERFACE SYSTEM PILOT STUDY A. Plot Study A plot clncal study of the BranGate Neural Interface System was ntated by Cyberknetcs Neurotechnology Systems, Inc. under a Food and Drug Admnstraton (FDA) Investgatonal Devce Exempton (IDE) and wth Insttutonal Revew Board (IRB) approvals; the studes began n May, Cauton: Investgatonal Devce. Lmted by Federal Law to Investgatonal Use B. Partcpants Clncal tral sessons of the BranGate NIS (Cyberknetcs Neurotechnology Systems, Inc.) were conducted by Cyberknetcs techncans wth two partcpants wth tetraplega (paralyss of both arms and both legs). Partcpant S3 was a 54 year old woman who had thromboss of the baslar artery and extensve pontne nfarcton nne years pror to tral recrutment. Partcpant A1 was a 37 year old man wth amyotrophc lateral scleross (ALS, motor neuron dsease), recruted to the tral sx years after beng dagnosed wth ALS. Both partcpants were rght hand domnant, and the ntracortcal array was placed n the left precentral gyrus n the regon of the arm representaton [1]. C. Recordng Sessons Durng each recordng sesson, neural sgnals were recorded from the motor cortex of the partcpants usng a chroncally-mplanted 96-channel Cyberknetcs mcroelectrode array and the BranGate NIS. After dgtzaton (30 khz per channel), real-tme, ampltude-thresholdng software was used to dentfy dfferent waveshapes on each channel [20]. Sngle neurons and mult-neuron actvty wth consstent waveforms [20] (both referred to here as unts ) were accepted or rejected for ncluson n the study at the begnnng of each sesson based on vsual nspecton of the solated waveforms. No further crtera were appled to dentfy sngle neurons. Durng neural recordng, partcpants vewed a computer montor that dsplayed task nformaton related to varous cursor control tasks as descrbed below.

3 III. TRAINING DECODING ALGORITHMS The overall tranng procedure was composed of a seres of short perods, called blocks, each of whch lasted mn. We devsed two types of tranng blocks: open-loop (OL) and closed-loop (CL). In OL blocks, a tranng cursor (TC) was dsplayed on a computer montor and moved to reach a target, generatng cursor movement trajectores. The tranng cursor was moved ether manually by a techncan or automatcally by a computer program. Durng the presentaton of the TC movement on the montor, the partcpants were nstructed to magne movng ther domnant arm or hand as f they were movng the TC. Followng the executon of multple OL blocks, we traned a decodng algorthm usng neural sgnals together wth the TC knematc data. Afterwards, n CL blocks, we presented not only the TC but also a feedback cursor (FC) whose movement was decoded from the partcpant s neural actvty usng the prevously traned decodng algorthm. We hypotheszed that showng the FC would prompt the partcpant to adjust ther neural actvty to mprove cursor control by sensng the error between the TC and the FC. The decodng algorthm was teratvely traned after every other CL block to ncorporate the potental adjustment of neural actvty nto the parameter estmaton. We utlzed two dfferent cursor movement tasks dependng on the decoded knematc parameter: a random pursut-trackng task, whch can generate a wde range of cursor poston data, was used for cursor poston decodng and a center-out-and-back task, whch can provde well-defned cursor velocty data, was used for cursor velocty (a) OL tranng CL tranng Testng decodng. In the latter case, the cursor speed followed a bell-shaped profle. Fgure 1 llustrates the tranng procedure and tasks. IV. NEURAL CORRELATION WITH CURSOR KINEMATICS The correlaton between neural frng rates and cursor knematc parameters, ncludng poston and velocty, was measured for every neuronal unt recorded durng the open-loop (OL) tranng blocks. The OL blocks were chosen because neural actvty was drectly related to vsualzed cursor knematcs and not assocated wth any partcular decodng algorthm. We evaluated correlaton usng the Pearson correlaton coeffcent (CC) whch provdes a drect measure wthout employng an explct model to fnd a mappng between frng rates and knematc parameters. In computaton of the CC, we searched over multple tme lags between each unt s frng rate and the knematc parameter and found the optmal lag yeldng the maxmum CC value. From the CC measures across multple recordng sessons n S3 and A1 (17 sessons, >1000 unts), we found that neuronal correlaton wth cursor velocty was stronger than wth cursor poston, more unts showed correlaton wth velocty, and >90% of all the recorded unts were statstcally correlated wth at least one of velocty or poston [10]. Fgure 2 shows some results of ths correlaton analyss. These results suggest that decodng cursor velocty, whch s more correlated wth neural actvty, may yeld better neural cursor control than decodng cursor poston. (a) Tranng cursor Feedback cursor Neural cursor Target (b) Random pursut-trackng Center-out-and-back (b) Fg. 1. (a) Tranng and testng procedure for human neural nterface systems (NISs). A neural cursor control tral sesson s composed of tranng and testng phases. The tranng phase s further dvded nto open-loop (OL) and closed-loop (CL) tranng. In OL tranng, the tranng cursor (TC) s moved on the montor by a techncan or a computer program to reach a target. In CL tranng, a feedback cursor (FC) s shown together wth the TC to provde feedback of how well cursor movement s decoded from neural actvty. In the testng phase, only a neurally controlled cursor (NC) s shown and moved by the partcpant to acqure a target. (b) Cursor movement tasks. A random pursut-trackng task s used to generate tranng data for poston decodng algorthms and a center-out-and-back task s used for velocty decodng algorthms. (Km et al. [10]). Fg. 2. (a) Correlaton coeffcents between ndvdual unts frng rates and cursor knematc parameters poston (whte) or velocty (gray). The medan wth 25% and 75% percentles are presented for each recordng sesson. (b) The number of unts showng sgnfcant correlaton (p < 0.01, t-test) wth cursor velocty or poston (Km et al. 2008). Tral day ndcates the day of recordng sesson after mplantaton. (Km et al. [10])

4 V. DECODING ALGORITHMS A decodng algorthm bult for an NIS extracts the movement ntenton of a subject from ther neural actvty and converts t to the knematc parameters requred to control a prosthetc devce. Such decodng algorthms draw on mathematcal models descrbng the (causal) relatonshp between neural actvty and knematcs. Most decodng algorthms have ther own parameters whch need to be determned through a tranng procedure. Here, tranng refers to a process through whch, a decodng algorthm, or more precsely the parameters of the algorthm, are traned to model a neural motor mappng. In ths paper, we compare two decodng algorthms, the lnear flter and the Kalman flter, both of whch have been wdely used n many human and non-human bran-computer nterface studes. We derve these two algorthms from a probablstc framework and llustrate man dfferences between them. Suppose x t s a D 1 vector of cursor knematcs such as poston [p x, p y ] T or velocty [v x, v y ] T n the x (horzontal) and y (vertcal) coordnates, sampled at a dscrete tme nstant t. Let z t be an N 1 vector of frng rates of N unts. The frng rate of a unt was estmated as the number of spkes n a fxed-sze tme wndow (e.g. 100ms) n our study. A goal n decodng s to fnd the most probable value of x t when we observe the entre hstory of frng rates, z 1:t-j where j denotes a tme lag between x t and z t. Ths can be represented as modelng a condtonal probablty, p(x t z 1:t-j ). The lnear flter acheves ths goal based on the maxmum lkelhood estmaton wth the assumptons of a lnear mappng and whte nose: t j t j T T x = a 0 + a z + ε = a 0 + a z + ε. (1) t = 1 t = t j L Here we assume that x t s correlated only wth L past values of z t-j. Snce ε t s regarded as a Gaussan random varable wth zero mean and varance of σ 2, (1) s rewrtten as, t j T 2 p( x t z t j L: t j ) = G x t a 0 a j z ;0, σ, (2) = t j L where G(τ; μ, σ 2 ) represents a Gaussan dstrbuton of τ wth mean μ and varance σ 2. The parameters, {a } are learned usng maxmum lkelhood estmaton. The Kalman flter models p(x t z 1:t-j ) usng a Bayesan formulaton, 1 p( xt z1 : t j ) = p( zt j xt ) p( xt xt 1) p( xt 1 zt 1 j ) dx (3) t 1 κ where κ s a normalzaton constant. p(x t z 1:t-j ) s nferred by fndng the condtonal mean of p(x t x t-1 ) n terms of the prevously estmated p(x t-1 z t-1-j ) and updatng t by p(z t-j x t ) whch carres nformaton of newly observed z t-j. In the Kalman flterng algorthm, p(x t x t-1 ) and p(z t-j x t ) are approxmated as lnear Gaussan models. The parameters n these models are learned from tranng data usng least squares. A key dfference between these two flters s that the t Kalman flter models the pror probablty of x t n p(x t x t-1 ) whereas the lnear flter does not. Ths pror model may be advantageous to decodng for NISs especally when we need to devse movement models wthout the knowledge of true movements. VI. CURSOR CONTROL IMPROVEMENT BY KALMAN VELOCITY DECODING In ths secton, we demonstrate how neural cursor control was mproved by choosng to decode cursor velocty (x t =[v x, v y ] T ) over poston and choosng to use the Kalman flter over the lnear flter. We evaluated cursor control performance n a four-target acquston task across 14 recordng sessons n S3 and A1. The partcpants were asked to move the neural cursor (NC) from center to one of four radally located targets and to hold the NC on the target for 500ms. The partcpants performed ths target acquston tral approxmately 80 tmes each sesson. In each tral, the partcpants had to acqure a target wthn 7s or the tral was deemed a falure. Fgure 3 llustrates neural cursor control performance usng the lnear poston flter versus the Kalman velocty flter. Cursor movement decoded wth the Kalman velocty flter was much smoother and straghter than that decoded wth the lnear poston flter. We quantfed cursor control performance by a number of measures, ncludng the number of drectonal changes and devaton of the cursor path from a straght lne trajectory. These measures confrmed that the Kalman velocty flter exhbted fewer drectonal changes and smaller devatons than the lnear poston flter [10]. In addton, the partcpants demonstrated more accurate target acquston performance wth the Kalman velocty flter, ncreasng the acquston rate by ~10%. The tme taken to reach the target was smlar for both flters. In fact, the cursor (a) (b) tral day 40, n = 179 tral day 48, n = 159 tral day 54, n = 147 tral day 254, n = 46 tral day 261, n = 80 tral day 282, n = 20 Fg. 3. Neural cursor trajectores durng four-target center-out task. Targets were acqured when the cursor dwelled on t for > 0.5s. Each lne shows a cursor trajectory startng from the center of the montor to each of four targets (yellow squares). n denotes the number of unts used n the decodng flter. (a) Examples of three sessons where the neural cursor poston was decoded by the lnear flter (b) Examples of three sessons where the neural cursor velocty was decoded by the Kalman flter. (Km et al. [10])

5 decoded by the lnear poston flter generally moved faster but ts erratc and curved moton meant t take long to reach and hold on the target. We further examned whether the performance mprovement of the Kalman velocty flter over the lnear poston flter was acheved due to the choce of knematc parameter or the choce of decodng algorthm. The partcpant S3 performed two center-out tasks, one wth the lnear velocty flter and the other wth the Kalman velocty flter, n a sngle sesson (performed three tmes over multple days). The results demonstrated that the lnear velocty flter generated smoother and more stable cursor movements than the lnear poston flter, whereas the Kalman velocty flter yelded slghtly better performance than the lnear velocty flter. Ths suggests that choosng to decode cursor velocty may contrbute more to cursor control mprovement than choosng to use the Kalman flter. VII. POINT AND CLICK CURSOR CONTROL To enable pont-and-clck cursor control from neural actvty, we have addressed a key queston of whether t s possble to decode multple states (.e. a contnuous pontng state and a dscrete clck state) smultaneously from a sngle neural populaton. We frst revsed the above tranng method by addng a new tranng phase for clck states [19]. In ths phase, the partcpant was asked to magne a specfc arm/hand movement (e.g. squeezng the hand) whenever a clck cue was shown on the montor. Next, we developed a new mult-state decodng algorthm based on the prevous work by Wood et al. [18]. We smplfed the probablstc mult-state model proposed n [18] to be sutable for real-tme applcatons. We represented the contnuous cursor state as cursor velocty and the dscrete state as one of two classes, {movement, clck}. We used the Kalman flter to decode cursor velocty and a lnear classfer to decode the dscrete state. The Kalman flter was traned usng the center-out task tranng samples (see Secton III). The lnear classfer was traned usng the data collected durng the clck tranng phase descrbed above. After tranng, mult-state decodng worked as follows: when the movement class was decoded from the classfer, the velocty sgnal from the Kalman flter was used to drve the cursor; when the clck class was decoded, the cursor was forced to stop wth zero velocty and a clck sgnal was generated [19]. We tested whether the mult-state decodng algorthm and the revsed tranng method could lead to pont-and-clck operaton of a computer cursor n humans wth tetraplega [19]. Over multple NIS study sessons, the partcpant (S3) performed a closed-loop target acquston task usng the clck functon generated by mult-state decodng. In ths task, one of eght radally located targets was hghlghted and the partcpant moved the neural cursor toward the target and clcked on the target to acqure t wthn a specfed maxmum tme (e.g. 9s). Over three recordng sessons, S3 successfully acqured ~97% of the targets by clckng on them, wth errors occurrng only due to tme lmt. There were no false clcks on ncorrect targets. False clcks on non-target screen regons were generated on average less than once per tral; many of these occurred close to the target. Note that t was not possble to determne whether these false clcks were generated as a result of naccurate dscrete state decodng or because that the partcpant ntended to clck but the cursor was not correctly postoned on the target. Fgure 4 llustrates neural cursor trajectores across three pont-and-clck sessons [19]. VIII. FUTURE WORK We have summarzed progress n neural decodng for ntracortcal NISs, enablng humans wth tetraplega to control arbtrary pont-to-pont movements of a computer cursor and clck on specfed target areas. There reman a number of scentfc and engneerng ssues that need to be resolved n order to develop a more relable and useful NIS. Here we dscuss several on-gong ssues partcularly related to neural decodng and our approaches to address them. Frst, only a few smple decodng models have been adopted so far for NISs, largely due to the fact that they are easly mplemented n real tme. These models assume a lnear relatonshp between neural actvty and knematc parameters whch does not generally hold when representng complex and nonlnear neural ensemble actvty. There have been several studes showng that nonlnear decodng models produced more accurate estmates of knematcs from motor cortcal sgnals [21-23]. Therefore, we are developng a nonlnear decodng model that better descrbes the relatonshp between neural and knematc data and also runs effectvely n a real-tme computng system. Among many nonlnear approaches, we are partcularly explorng the nonlnear representaton and decodng of neural actvty usng Gaussan processes [24], nonlnear dynamc system models [25] and partcle flters [15]. Second, the current decodng models and tranng paradgms have not explctly focused on decodng cursor speed. Ths may account for the somewhat slow cursor movement seen n our experments. Ths problem s partally assocated wth the aforementoned lnearty assumpton. For tral day 292, n = 37 tral day 301, n = 38 tral day 303, n = 57 Fg. 4. Neural cursor trajectores wth pont-and-clck cursor control. Targets were acqured when the partcpant clcked on the target wth ther neural actvty. Black lnes are cursor trajectory from center to each of eght targets and yellow crcles are targets. n denotes the number of unts (Km et al. [19])

6 the Kalman flter case, the lnear Gaussan model used to descrbe how movement changes n tme appears to be too smple to represent cursor dynamcs. Consequently, the velocty profle of the neurally controlled cursor movement s dfferent from the dealzed ballstc speed profle whch s generally observed n real arm movements. To address ths ssue, we are seekng a new probablstc movement pror model that may effectvely represent cursor dynamcs, resultng n cursor moton more responsve to the ntenton of a user. Fnally, the current paradgm of tranng and usng decodng models freezes the model parameters after tranng s fnshed. Ths paradgm assumes that the statstcal and encodng propertes of neural sgnals do not change sgnfcantly before and after the end of tranng. However, decodng performance could decrease f any of those propertes sgnfcantly change after tranng. Our group has shown n an off-lne non-human prmate data analyss that the parameters of a lnear functon relatng neural frng rates to knematc parameters sgnfcantly changed over short-tme perods [26]. We seek to address ths non-statonarty by developng an adaptve decodng flter that constantly adjusts ts parameters accordng to the change n the statstcal and encodng propertes of the neural sgnals. We beleve that ths adaptve decodng flter wll enable NIS cursor control that s more robust to the non-statonarty of neural sgnals. ACKNOWLEDGMENT We thank Gregory Shakhnarovch, Frank Wood and Wlson Truccolo for comments and assstance. We also thank Abe Caplan, Erc Peterson, Mchael Frtz and Cyberknetcs Neurotechnology Systems, Inc. for runnng the recordng sessons descrbed here. REFERENCES [1] L.R. Hochberg et al., Neuronal ensemble control of prosthetc devces by a human wth tetraplega, Nature, vol. 442, pp , [2] D.R Humphrey, D.M. Schmdt and W.D. 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