02336nas a2200229   4500008004100000022001400041245012600055210006900181260001600250490000700266520152600273653001001799653002501809653002701834653001101861653004101872100001601913700001901929700001901948700001901967856012001986       2024                        eng d  a2050-084X00aNovel cyclic homogeneous oscillation detection method for high accuracy and specific characterization of neural dynamics.0 aNovel cyclic homogeneous oscillation detection method for high a  c2024 Sep 060 v123 a<p>Determining the presence and frequency of neural oscillations is essential to understanding dynamic brain function. Traditional methods that detect peaks over 1/ noise within the power spectrum fail to distinguish between the fundamental frequency and harmonics of often highly non-sinusoidal neural oscillations. To overcome this limitation, we define fundamental criteria that characterize neural oscillations and introduce the cyclic homogeneous oscillation (CHO) detection method. We implemented these criteria based on an autocorrelation approach to determine an oscillation's fundamental frequency. We evaluated CHO by verifying its performance on simulated non-sinusoidal oscillatory bursts and validated its ability to determine the fundamental frequency of neural oscillations in electrocorticographic (ECoG), electroencephalographic (EEG), and stereoelectroencephalographic (SEEG) signals recorded from 27 human subjects. Our results demonstrate that CHO outperforms conventional techniques in accurately detecting oscillations. In summary, CHO demonstrates high precision and specificity in detecting neural oscillations in time and frequency domains. The method's specificity enables the detailed study of non-sinusoidal characteristics of oscillations, such as the degree of asymmetry and waveform of an oscillation. Furthermore, CHO can be applied to identify how neural oscillations govern interactions throughout the brain and to determine oscillatory biomarkers that index abnormal brain function.</p>10aBrain10aElectrocorticography10aElectroencephalography10aHumans10aSignal Processing, Computer-Assisted1 aCho, Hohyun1 aAdamek, Markus1 aWillie, Jon, T1 aBrunner, Peter  uhttps://neurotechcenter.org/publications/2024/novel-cyclic-homogeneous-oscillation-detection-method-high-accuracy-002427nas a2200349   4500008004100000022001400041245011100055210006900166260001200235300001100247490000700258520132200265653001501587653002801602653003001630653003701660653002701697653002901724653001101753653001601764653001701780653001301797653003501810653003101845653003201876653004101908653002901949100001201978700001301990700002602003856004802029       2014                        eng d  a1558-021000aAdaptive spatio-temporal filtering for movement related potentials in EEG-based brain-computer interfaces.0 aAdaptive spatiotemporal filtering for movement related potential  c07/2014  a847-570 v223 aMovement related potentials (MRPs) are used as features in many brain-computer interfaces (BCIs) based on electroencephalogram (EEG). MRP feature extraction is challenging since EEG is noisy and varies between subjects. Previous studies used spatial and spatio-temporal filtering methods to deal with these problems. However, they did not optimize temporal information or may have been susceptible to overfitting when training data are limited and the feature space is of high dimension. Furthermore, most of these studies manually select data windows and low-pass frequencies. We propose an adaptive spatio-temporal (AST) filtering method to model MRPs more accurately in lower dimensional space. AST automatically optimizes all parameters by employing a Gaussian kernel to construct a low-pass time-frequency filter and a linear ridge regression (LRR) algorithm to compute a spatial filter. Optimal parameters are simultaneously sought by minimizing leave-one-out cross-validation error through gradient descent. Using four BCI datasets from 12 individuals, we compare the performances of AST filter to two popular methods: the discriminant spatial pattern filter and regularized spatio-temporal filter. The results demonstrate that our AST filter can make more accurate predictions and is computationally feasible.10aAlgorithms10aArtificial Intelligence10abrain-computer interfaces10aData Interpretation, Statistical10aElectroencephalography10aEvoked Potentials, Motor10aHumans10aImagination10aMotor Cortex10aMovement10aPattern Recognition, Automated10aReproducibility of Results10aSensitivity and Specificity10aSignal Processing, Computer-Assisted10aSpatio-Temporal Analysis1 aLu, Jun1 aXie, Kan1 aMcFarland, Dennis, J.  uhttp://www.ncbi.nlm.nih.gov/pubmed/2472363204066nas a2200277   4500008004100000022001400041245007100055210006900126260001200195300001100207490000700218520327200225653001003497653002003507653002703527653001103554653001103565653001603576653000903592653004103601653002803642100002703670700001803697700002503715856004803740       2011                        eng d  a1095-957200aCausal influence of gamma oscillations on the sensorimotor rhythm.0 aCausal influence of gamma oscillations on the sensorimotor rhyth  c05/2011  a837-420 v563 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Gamma oscillations of the electromagnetic field of the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;are known to be involved in a variety of cognitive processes, and are believed to be fundamental for information processing within the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">. While gamma oscillations have been shown to be correlated with&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;rhythms at different frequencies, to date no empirical evidence has been presented that supports a causal influence of gamma oscillations on other&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;rhythms. In this work, we study the relation of gamma oscillations and the sensorimotor rhythm (SMR) in healthy human subjects using electroencephalography. We first demonstrate that modulation of the SMR, induced by motor imagery of either the left or right hand, is positively correlated with the power of frontal and occipital gamma oscillations, and negatively correlated with the power of centro-parietal gamma oscillations. We then demonstrate that the most simple causal structure, capable of explaining the observed correlation of gamma oscillations and the SMR, entails a causal influence of gamma oscillations on the SMR. This finding supports the fundamental role attributed to gamma oscillations for information processing within the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">, and is of particular importance for&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain-computer interfaces</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;(BCIs). As modulation of the SMR is typically used in BCIs to infer a subject's intention, our findings entail that gamma oscillations have a causal influence on a subject's capability to utilize a&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">BCI</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;for means of communication.</span></p>10aAdult10aCerebral Cortex10aElectroencephalography10aFemale10aHumans10aImagination10aMale10aSignal Processing, Computer-Assisted10aUser-Computer Interface1 aGrosse-Wentrup, Moritz1 aSchölkopf, B1 aHill, Jeremy, Jeremy  uhttp://www.ncbi.nlm.nih.gov/pubmed/2045162602954nas a2200373   4500008004100000022001400041245008200055210006900137260001200206300001100218490000700229520184300236653002802079653002702107653002802134653002702162653002202189653003002211653001102241653001302252653002502265653002402290653001202314653004102326653002802367653001802395653002202413100001902435700001502454700002502469700002002494700001802514856004802532       2011                        eng d  a1530-888X00aA graphical model framework for decoding in the visual ERP-based BCI speller.0 agraphical model framework for decoding in the visual ERPbased BC  c01/2011  a160-820 v233 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">We present a graphical model framework for decoding in the visual ERP-based speller system. The proposed framework allows researchers to build generative models from which the decoding rules are obtained in a straightforward manner. We suggest two models for generating&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;signals conditioned on the stimulus events. Both models incorporate letter frequency information but assume different dependencies between&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;signals and stimulus events. For both models, we derive decoding rules and perform a discriminative training. We show on real visual speller data how decoding performance improves by incorporating letter frequency information and using a more realistic graphical model for the dependencies between the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;signals and the stimulus events. Furthermore, we discuss how the standard approach to decoding can be seen as a special case of the graphical model framework. The letter also gives more insight into the discriminative approach for decoding in the visual speller system.</span></p>10aArtificial Intelligence10aComputer User Training10aDiscrimination Learning10aElectroencephalography10aEvoked Potentials10aEvoked Potentials, Visual10aHumans10aLanguage10aModels, Neurological10aModels, Theoretical10aReading10aSignal Processing, Computer-Assisted10aUser-Computer Interface10aVisual Cortex10aVisual Perception1 aMartens, S M M1 aMooij, J M1 aHill, Jeremy, Jeremy1 aFarquhar, Jason1 aSchölkopf, B  uhttp://www.ncbi.nlm.nih.gov/pubmed/2096454003279nas a2200337   4500008004100000022001400041245011400055210006900169260001200238300001200250490000700262520231800269653001502587653001002602653001002612653001802622653002702640653001102667653001102678653000902689653001602698653004102714653002002755100001802775700001902793700002202812700001902834700002102853700001902874856004802893       2011                        eng d  a1095-957200aSpatiotemporal dynamics of electrocorticographic high gamma activity during overt and covert word repetition.0 aSpatiotemporal dynamics of electrocorticographic high gamma acti  c02/2011  a2960-720 v543 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Language is one of the defining abilities of humans. Many studies have characterized the neural correlates of different aspects of language processing. However, the imaging techniques typically used in these studies were limited in either their temporal or spatial resolution. Electrocorticographic (ECoG) recordings from the surface of the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;combine high spatial with high temporal resolution and thus could be a valuable tool for the study of neural correlates of language function. In this study, we defined the spatiotemporal dynamics of ECoG activity during a word repetition task in nine human subjects. ECoG was recorded while each subject overtly or covertly repeated words that were presented either visually or auditorily. ECoG amplitudes in the high gamma (HG) band confidently tracked neural changes associated with stimulus presentation and with the subject's verbal response. Overt word production was primarily associated with HG changes in the superior and middle parts of temporal lobe, Wernicke's area, the supramarginal gyrus, Broca's area, premotor cortex (PMC), primary motor cortex. Covert word production was primarily associated with HG changes in superior temporal lobe and the supramarginal gyrus. Acoustic processing from both auditory stimuli as well as the subject's own voice resulted in HG power changes in superior temporal lobe and Wernicke's area. In summary, this study represents a comprehensive characterization of overt and covert speech using electrophysiological imaging with high spatial and temporal resolution. It thereby complements the findings of previous neuroimaging studies of language and thus further adds to&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">current</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;understanding of word processing in humans.</span></p>10aAdolescent10aAdult10aBrain10aBrain Mapping10aElectroencephalography10aFemale10aHumans10aMale10aMiddle Aged10aSignal Processing, Computer-Assisted10aVerbal Behavior1 aPei, Xiao-Mei1 aLeuthardt, E C1 aGaona, Charles, M1 aBrunner, Peter1 aWolpaw, Jonathan1 aSchalk, Gerwin  uhttp://www.ncbi.nlm.nih.gov/pubmed/2102978404388nas a2200385   4500008004100000022001400041245009500055210006900150260001200219300001100231490000800242520325500250653001003505653003403515653002103549653001003570653003603580653002403616653002703640653002103667653001103688653000903699653004103708653002803749100002603777700002503803700001403828700001903842700001403861700001503875700002503890700002103915700001803936856004803954       2011                        eng d  a1872-895200aTransition from the locked in to the completely locked-in state: a physiological analysis.0 aTransition from the locked in to the completely lockedin state a  c06/2011  a925-330 v1223 a<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">OBJECTIVE:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">To clarify the physiological and behavioral boundaries between locked-in (LIS) and the completely locked-in state (CLIS) (no voluntary eye movements, no communication possible) through electrophysiological data and to secure&nbsp;<span class="highlight">brain-computer-interface</span>&nbsp;(<span class="highlight">BCI</span>) communication.</p>
<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">METHODS:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Electromyography from facial muscles, external anal sphincter (EAS), electrooculography and electrocorticographic data during different psychophysiological tests were acquired to define electrophysiological differences in an amyotrophic lateral sclerosis (ALS) patient with an intracranially implanted grid of 112 electrodes for nine months while the patient passed from the LIS to the CLIS.</p>
<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">RESULTS:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">At the very end of the LIS there was no facial muscle activity, nor external anal sphincter but eye control. Eye movements were slow and lasted for short periods only. During CLIS event related&nbsp;<span class="highlight">brain</span>potentials (ERP) to passive limb movements and auditory stimuli were recorded, vibrotactile stimulation of different body parts resulted in no ERP response.</p>
<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">CONCLUSIONS:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">The results presented contradict the commonly accepted assumption that the EAS is the last remaining muscle under voluntary control and demonstrate complete loss of eye movements in CLIS. The eye muscle was shown to be the last muscle group under voluntary control. The findings suggest ALS as a multisystem disorder, even affecting afferent sensory pathways.</p>
<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">SIGNIFICANCE:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Auditory and proprioceptive&nbsp;<span class="highlight">brain-computer-interface</span>&nbsp;(<span class="highlight">BCI</span>) systems are the only remaining communication channels in CLIS.</p>10aAdult10aAmyotrophic Lateral Sclerosis10aArea Under Curve10aBrain10aCommunication Aids for Disabled10aDisease Progression10aElectroencephalography10aElectromyography10aHumans10aMale10aSignal Processing, Computer-Assisted10aUser-Computer Interface1 aMurguialday, Ramos, A1 aHill, Jeremy, Jeremy1 aBensch, M1 aMartens, S M M1 aHalder, S1 aNijboer, F1 aSchoelkopf, Bernhard1 aBirbaumer, Niels1 aGharabaghi, A  uhttp://www.ncbi.nlm.nih.gov/pubmed/2088829201606nas a2200301   4500008004100000022001400041245007000055210006600125260001200191300001200203490000700215520071500222653001000937653002100947653002700968653002200995653001101017653002501028653003101053653004101084653001701125653002801142100002001170700002301190700001901213700002401232856004801256       2010                        eng d  a1558-253100aA procedure for measuring latencies in brain-computer interfaces.0 aprocedure for measuring latencies in braincomputer interfaces  c06/2010  a1785-970 v573 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Brain-computer interface (BCI) systems must process neural signals with consistent timing in order to support adequate system performance. Thus, it is important to have the capability to determine whether a particular BCI configuration (i.e., hardware and software) provides adequate timing performance for a particular experiment. This report presents a method of measuring and quantifying different aspects of system timing in several typical BCI experiments across a range of settings, and presents comprehensive measures of expected overall system latency for each experimental configuration.</span></p>10aBrain10aComputer Systems10aElectroencephalography10aEvoked Potentials10aHumans10aModels, Neurological10aReproducibility of Results10aSignal Processing, Computer-Assisted10aTime Factors10aUser-Computer Interface1 aWilson, Adam, J1 aMellinger, Jürgen1 aSchalk, Gerwin1 aWilliams, Justin, C  uhttp://www.ncbi.nlm.nih.gov/pubmed/2040378101322nas a2200265   4500008004100000022001400041245005600055210005400111260000900165300001300174490000900187520048800196653001500684653001000699653002400709653002100733653003100754653001900785653003100804653003200835653004100867653002800908100001900936856010100955       2009                        eng d  a1557-170X00aEffective brain-computer interfacing using BCI2000.0 aEffective braincomputer interfacing using BCI2000  c2009  a5498-5010 v20093 aTo facilitate research and development in Brain-Computer Interface (BCI) research, we have been developing a general-purpose BCI system, called BCI2000, over the past nine years. This system has enjoyed a growing adoption in BCI and related areas and has been the basis for some of the most impressive studies reported to date. This paper gives an update on the status of this project by describing the principles of the BCI2000 system, its benefits, and impact on the field to date.10aAlgorithms10aBrain10aElectrocardiography10aEquipment Design10aEquipment Failure Analysis10aRehabilitation10aReproducibility of Results10aSensitivity and Specificity10aSignal Processing, Computer-Assisted10aUser-Computer Interface1 aSchalk, Gerwin  uhttps://neurotechcenter.org/publications/2009/effective-brain-computer-interfacing-using-bci200002156nas a2200337   4500008004100000022001400041245008300055210006900138260001200207300000700219490000700226520120000233653001001433653002001443653001101463653002401474653001701498653001301515653002301528653002401551653002801575653001301603653004101616653002801657100001901685700001901704700001801723700001601741700001301757856004801770       2009                        eng d  a1092-068400aEvolution of brain-computer interfaces: going beyond classic motor physiology.0 aEvolution of braincomputer interfaces going beyond classic motor  c07/2009  aE40 v273 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">The notion that a computer can decode brain signals to infer the intentions of a human and then enact those intentions directly through a machine is becoming a realistic technical possibility. These types of devices are known as brain-computer interfaces (BCIs). The evolution of these neuroprosthetic technologies could have significant implications for patients with motor disabilities by enhancing their ability to interact and communicate with their environment. The cortical physiology most investigated and used for device control has been brain signals from the primary motor cortex. To date, this classic motor physiology has been an effective substrate for demonstrating the potential efficacy of BCI-based control. However, emerging research now stands to further enhance our understanding of the cortical physiology underpinning human intent and provide further signals for more complex brain-derived control. In this review, the authors report the current status of BCIs and detail the emerging research trends that stand to augment clinical applications in the future.</span></p>10aBrain10aCerebral Cortex10aHumans10aMan-Machine Systems10aMotor Cortex10aMovement10aMovement Disorders10aNeuronal Plasticity10aProstheses and Implants10aResearch10aSignal Processing, Computer-Assisted10aUser-Computer Interface1 aLeuthardt, E C1 aSchalk, Gerwin1 aRoland, Jarod1 aRouse, Adam1 aMoran, D  uhttp://www.ncbi.nlm.nih.gov/pubmed/1956989202457nas a2200349   4500008004100000022001400041245012600055210006900181260001200250300001200262490000700274520141700281653001501698653001001713653001801723653002601741653002101767653001301788653002401801653000901825653001101834653001801845653001901863653003101882653002301913653004101936100002001977700002301997700002002020700001902040856004802059       2009                        eng d  a1879-278200aMapping broadband electrocorticographic recordings to two-dimensional hand trajectories in humans Motor control features.0 aMapping broadband electrocorticographic recordings to twodimensi  c11/2009  a1257-700 v223 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Brain-machine interfaces (BMIs) aim to translate the motor intent of locked-in patients into neuroprosthetic control commands. Electrocorticographical (ECoG) signals provide promising neural inputs to BMIs as shown in recent studies. In this paper, we utilize a broadband spectrum above the fast gamma ranges and systematically study the role of spectral resolution, in which the broadband is partitioned, on the reconstruction of the patients' hand trajectories. Traditionally, the power of ECoG rhythms (&lt;200-300 Hz) has been computed in short duration bins and instantaneously and linearly mapped to cursor trajectories. Neither time embedding, nor nonlinear mappings have been previously implemented in ECoG neuroprosthesis. Herein, mapping of neural modulations to goal-oriented motor behavior is achieved via linear adaptive filters with embedded memory depths and as a novelty through echo state networks (ESNs), which provide nonlinear mappings without compromising training complexity or increasing the number of model parameters, with up to 85% correlation. Reconstructed hand trajectories are analyzed through spatial, spectral and temporal sensitivities. The superiority of nonlinear mappings in the cases of low spectral resolution and abundance of interictal activity is discussed.</span></p>10aAlgorithms10aBrain10aBrain Mapping10aElectrodes, Implanted10aElectrodiagnosis10aEpilepsy10aFeasibility Studies10aHand10aHumans10aLinear Models10aMotor Activity10aNeural Networks (Computer)10aNonlinear Dynamics10aSignal Processing, Computer-Assisted1 aGunduz, Aysegul1 aSanchez, Justin, C1 aCarney, Paul, R1 aPrincipe, Jose  uhttp://www.ncbi.nlm.nih.gov/pubmed/1964798101758nas a2200361   4500008004100000022001400041245012300055210006900178260001200247300001100259490000600270520064200276653001500918653001000933653001400943653002400957653002700981653003501008653001101043653002501054653003501079653002301114653001401137653004101151653003401192653002801226653001201254100001901266700002501285700002001310700001801330856004801348       2009                        eng d  a1741-255200aOverlap and refractory effects in a brain-computer interface speller based on the visual P300 event-related potential.0 aOverlap and refractory effects in a braincomputer interface spel  c04/2009  a0260030 v63 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">We reveal the presence of refractory and overlap effects in the event-related potentials in visual P300 speller datasets, and we show their negative impact on the performance of the system. This finding has important implications for how to encode the letters that can be selected for communication. However, we show that such effects are dependent on stimulus parameters: an alternative stimulus type based on apparent motion suffers less from the refractory effects and leads to an improved letter prediction performance.</span></p>10aAlgorithms10aBrain10aCognition10aComputer Simulation10aElectroencephalography10aEvent-Related Potentials, P30010aHumans10aModels, Neurological10aPattern Recognition, Automated10aPhotic Stimulation10aSemantics10aSignal Processing, Computer-Assisted10aTask Performance and Analysis10aUser-Computer Interface10aWriting1 aMartens, S M M1 aHill, Jeremy, Jeremy1 aFarquhar, Jason1 aSchölkopf, B  uhttp://www.ncbi.nlm.nih.gov/pubmed/1925546202116nas a2200277   4500008004100000022001400041245011300055210006900168260001200237300001500249490000700264520130300271653001001574653002701584653001201611653001101623653000901634653001901643653004101662653001601703100001801719700002001737700001801757700001501775856004801790       2009                        eng d  a1001-551500aPower spectrum analysis on the multiparameter electroencephalogram features of physiological mental fatigue.0 aPower spectrum analysis on the multiparameter electroencephalogr  c02/2009  a162-6, 1720 v263 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">The aim of this experiment is to find a feasible impersonal index for analyzing the physiological mental fatigue level. Three characteristic parameters, relative power in different rhythm, barycenter frequency and power spectral entropy, are extracted from two channels' electroencephalogram (EEG) under two physiological mental fatigue states. Then relationships between such three parameters and physiological mental fatigue are analyzed to explore whether they can be of use for detecting (or monitoring) the mental fatigue level. The experiment results show that the relative power, barycenter frequency and power spectral entropy of EEG exhibit strong correlation with physiological mental fatigue level. While physiological mental fatigue level increases, the relative power in theta, alpha and beta rhythms, barycenter frequency and power spectral entropy of EEG decrease, but the relative power in delta rhythm of EEG increases. The relative power in four rhythms, barycenter frequency and power spectral entropy of EEG reflect the change of physiological mental fatigue level sensitively, and may hopefully be used as indexes for detecting physiological mental fatigue level.</span></p>10aAdult10aElectroencephalography10aEntropy10aHumans10aMale10aMental Fatigue10aSignal Processing, Computer-Assisted10aYoung Adult1 aZhang, Ai-hua1 aZheng, Shi Dong1 aPei, Xiao-Mei1 aOuyang, Yi  uhttp://www.ncbi.nlm.nih.gov/pubmed/1933457702591nas a2200433   4500008004100000022001400041245012500055210006900180260001200249300001100261490000700272520133300279653001001612653001801622653002001640653002501660653002601685653002701711653001301738653001101751653001101762653000901773653001601782653003301798653004101831653001601872100001901888700001801907700002201925700002201947700002001969700002401989700002302013700002002036700001902056700001502075700001902090856004802109       2009                        eng d  a1525-506900aA practical procedure for real-time functional mapping of eloquent cortex using electrocorticographic signals in humans.0 apractical procedure for realtime functional mapping of eloquent   c07/2009  a278-860 v153 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Functional mapping of eloquent cortex is often necessary prior to invasive brain surgery, but current techniques that derive this mapping have important limitations. In this article, we demonstrate the first comprehensive evaluation of a rapid, robust, and practical mapping system that uses passive recordings of electrocorticographic signals. This mapping procedure is based on the BCI2000 and SIGFRIED technologies that we have been developing over the past several years. In our study, we evaluated 10 patients with epilepsy from four different institutions and compared the results of our procedure with the results derived using electrical cortical stimulation (ECS) mapping. The results show that our procedure derives a functional motor cortical map in only a few minutes. They also show a substantial concurrence with the results derived using ECS mapping. Specifically, compared with ECS maps, a next-neighbor evaluation showed no false negatives, and only 0.46 and 1.10% false positives for hand and tongue maps, respectively. In summary, we demonstrate the first comprehensive evaluation of a practical and robust mapping procedure that could become a new tool for planning of invasive brain surgeries.</span></p>10aAdult10aBrain Mapping10aCerebral Cortex10aElectric Stimulation10aElectrodes, Implanted10aElectroencephalography10aEpilepsy10aFemale10aHumans10aMale10aMiddle Aged10aPractice Guidelines as Topic10aSignal Processing, Computer-Assisted10aYoung Adult1 aBrunner, Peter1 aRitaccio, A L1 aLynch, Timothy, M1 aEmrich, Joseph, F1 aWilson, Adam, J1 aWilliams, Justin, C1 aAarnoutse, Erik, J1 aRamsey, Nick, F1 aLeuthardt, E C1 aBischof, H1 aSchalk, Gerwin  uhttp://www.ncbi.nlm.nih.gov/pubmed/1936663805137nas a2200373   4500008004100000022001400041245007500055210006900130260001200199300001000211490000800221520406800229653001004297653001504307653001004322653001804332653002404350653002704374653001104401653000904412653002404421653002404445653001904469653003604488653004104524653002404565653002804589100001904617700001904636700002404655700001504679700002104694856004804715       2008                        eng d  a0165-027000aBrain-computer interfaces (BCIs): Detection Instead of Classification.0 aBraincomputer interfaces BCIs Detection Instead of Classificatio  c01/2008  a51-620 v1673 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Many studies over the past two decades have shown that people can use&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;signals to convey their intent to a&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">computer</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;through&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain-computer interfaces</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;(BCIs). These devices operate by recording signals from the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;and translating these signals into device commands. They can be used by people who are severely paralyzed to communicate without any use of muscle activity. One of the major impediments in translating this novel technology into&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">clinical</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;applications is the current requirement for preliminary analyses to identify the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;signal features best suited for communication. This paper introduces and validates signal detection, which does not require such analysis procedures, as a new concept in BCI signal processing. This detection concept is realized with Gaussian mixture models (GMMs) that are used to model resting&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;activity so that any change in&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">relevant</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;signals can be detected. It is implemented in a package called SIGFRIED (SIGnal modeling For Real-time Identification and Event Detection). The results indicate that SIGFRIED produces results that are within the range of those achieved using a common analysis strategy that requires preliminary identification of signal features. They indicate that such laborious analysis procedures could be replaced by merely recording&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;signals during rest. In summary, this paper demonstrates how SIGFRIED could be used to overcome one of the present impediments to translation of laboratory BCI demonstrations into clinically practical applications.</span></p>10aAdult10aAlgorithms10aBrain10aBrain Mapping10aElectrocardiography10aElectroencephalography10aHumans10aMale10aMan-Machine Systems10aNormal Distribution10aOnline Systems10aSignal Detection, Psychological10aSignal Processing, Computer-Assisted10aSoftware Validation10aUser-Computer Interface1 aSchalk, Gerwin1 aBrunner, Peter1 aGerhardt, Lester, A1 aBischof, H1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/1792013405892nas a2200361   4500008004100000022001400041245010100055210006900156260001200225300001000237490000800247520480000255653001505055653002805070653001805098653002005116653002705136653002405163653001105187653000905198653001105207653003105218653003205249653002805281653004105309653002205350653002805372100002305400700002005423700002005443700001905463856004805482       2008                        eng d  a0165-027000aExtraction and localization of mesoscopic motor control signals for human ECoG neuroprosthetics.0 aExtraction and localization of mesoscopic motor control signals   c01/2008  a63-810 v1673 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Electrocorticogram (</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">ECoG</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">) recordings for&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">neuroprosthetics</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;provide a&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">mesoscopic</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;level of abstraction of brain function between microwire single neuron recordings and the electroencephalogram (EEG). Single-trial&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">ECoG</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;neural interfaces require appropriate feature&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">extraction</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;and signal processing methods to identify and model in real-time signatures of&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">motor</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;events in spontaneous brain activity. Here, we develop the clinical experimental paradigm and analysis tools to record broadband (1Hz to 6kHz)&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">ECoG</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;from patients participating in a reaching and pointing task. Motivated by the significant role of amplitude modulated rate coding in extracellular spike based brain-machine interfaces (BMIs), we develop methods to quantify spatio-temporal intermittent increased&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">ECoG</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;voltages to determine if they provide viable&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">control</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;inputs for&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">ECoG</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;neural interfaces. This study seeks to explore preprocessing modalities that emphasize amplitude modulation across frequencies and channels in the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">ECoG</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;above the level of noisy background fluctuations in order to derive the commands for complex, continuous&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">control</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;tasks. Preliminary experiments show that it is possible to derive online predictive models and spatially localize the generation of commands in the cortex for&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">motor</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;tasks using amplitude modulated&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">ECoG</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">.</span></p>10aAdolescent10aBiofeedback, Psychology10aBrain Mapping10aCerebral Cortex10aElectroencephalography10aEpilepsies, Partial10aFemale10aHand10aHumans10aMagnetic Resonance Imaging10aPhysical Therapy Modalities10aPsychomotor Performance10aSignal Processing, Computer-Assisted10aSpectrum Analysis10aUser-Computer Interface1 aSanchez, Justin, C1 aGunduz, Aysegul1 aCarney, Paul, R1 aPrincipe, Jose  uhttp://www.ncbi.nlm.nih.gov/pubmed/1758250703153nas a2200481   4500008004100000022001400041245008200055210006900137260001200206300001000218490000700228520180600235653001002041653002802051653002002079653003602099653002402135653002702159653002402186653001102210653001102221653001602232653000902248653001602257653001902273653001702292653004102309653001302350653002502363653001702388653002802405653001202433100002002445700001802465700001302483700002502496700002402521700001802545700001802563700002102581700002102602856004802623       2008                        eng d  a1525-506900aVoluntary brain regulation and communication with electrocorticogram signals.0 aVoluntary brain regulation and communication with electrocortico  c08/2008  a300-60 v133 a<p><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Brain-computer interfaces</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;(BCIs) can be used for communication in writing without muscular activity or for learning to control seizures by voluntary regulation of&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;signals such as the electroencephalogram (EEG). Three of five patients with epilepsy were able to spell their names with electrocorticogram (ECoG) signals derived from motor-related areas within only one or two training sessions. Imagery of finger or tongue movements was classified with support-vector classification of autoregressive coefficients derived from the ECoG signals. After training of the classifier, binary classification responses were used to select letters from a&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">computer</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">-generated menu. Offline analysis showed increased theta activity in the unsuccessful patients, whereas the successful patients exhibited dominant sensorimotor rhythms that they could control. The high spatial resolution and increased signal-to-noise ratio in ECoG signals, combined with short training periods, may offer an alternative for communication in complete paralysis, locked-in syndrome, and motor restoration.</span></p>10aAdult10aBiofeedback, Psychology10aCerebral Cortex10aCommunication Aids for Disabled10aDominance, Cerebral10aElectroencephalography10aEpilepsies, Partial10aFemale10aHumans10aImagination10aMale10aMiddle Aged10aMotor Activity10aMotor Cortex10aSignal Processing, Computer-Assisted10aSoftware10aSomatosensory Cortex10aTheta Rhythm10aUser-Computer Interface10aWriting1 aHinterberger, T1 aWidman, Guido1 aLal, T N1 aHill, Jeremy, Jeremy1 aTangermann, Michael1 aRosenstiel, W1 aSchölkopf, B1 aElger, Christian1 aBirbaumer, Niels  uhttp://www.ncbi.nlm.nih.gov/pubmed/1849554104391nas a2200409   4500008004100000022001400041245010700055210006900162260001200231300002900243490000700272520323000279653001003509653002203519653001803541653002503559653002603584653002703610653001103637653000903648653001103657653000903668653001603677653001703693653001703710653004103727653001103768100001903779700002003798700002603818700001903844700002003863700002003883700001303903700001703916856004803933       2007                        eng d  a1524-404000aElectrocorticographic Frequency Alteration Mapping: A Clinical Technique for Mapping the Motor Cortex.0 aElectrocorticographic Frequency Alteration Mapping A Clinical Te  c04/2007  a260-70; discussion 270-10 v603 a<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">OBJECTIVE:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Electrocortical stimulation (ECS) has been well established for delineating the eloquent cortex. However, ECS is still coarse and inefficient in delineating regions of the functional cortex and can be hampered by after-discharges. Given these constraints, an adjunct approach to defining the motor cortex is the use of electrocorticographic signal changes associated with active regions of the cortex. The broad range of frequency oscillations are categorized into two main groups with respect to the sensorimotor cortex: low and high frequency bands. The low frequency bands tend to show a power reduction with cortical activation, whereas the high frequency bands show power increases. These power changes associated with the activated cortex could potentially provide a powerful tool in delineating areas of the motor cortex. We explore electrocorticographic signal alterations as they occur with activated regions of the motor cortex, as well as its potential in clinical brain mapping applications.</p>
<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">METHODS:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">We evaluated seven patients who underwent invasive monitoring for seizure localization. Each patient had extraoperative ECS mapping to identify the motor cortex. All patients also performed overt hand and tongue motor tasks to identify associated frequency power changes in regard to location and degree of concordance with ECS results that localized either hand or tongue motor function.</p>
<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">RESULTS:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">The low frequency bands had a high sensitivity (88.9-100%) and a lower specificity (79.0-82.6%) for identifying electrodes with either hand or tongue ECS motor responses. The high frequency bands had a lower sensitivity (72.7-88.9%) and a higher specificity (92.4-94.9%) in correlation with the same respective ECS positive electrodes.</p>
<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">CONCLUSION:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">The concordance between stimulation and spectral power changes demonstrate the possible utility of electrocorticographic frequency alteration mapping as an adjunct method to improve the efficiency and resolution of identifying the motor cortex.</p>10aAdult10aBiological Clocks10aBrain Mapping10aElectric Stimulation10aElectrodes, Implanted10aElectroencephalography10aFemale10aHand10aHumans10aMale10aMiddle Aged10aMotor Cortex10aOscillometry10aSignal Processing, Computer-Assisted10aTongue1 aLeuthardt, E C1 aMiller, John, W1 aAnderson, Nicholas, R1 aSchalk, Gerwin1 aDowling, Joshua1 aMiller, John, W1 aMoran, D1 aOjemann, J G  uhttp://www.ncbi.nlm.nih.gov/pubmed/1741516203540nas a2200457   4500008004100000022001400041245004900055210004100104260001200145300001100157490000700168520234400175653001002519653001502529653001402544653001002558653002702568653002702595653002102622653001302643653001102656653000902667653000902676653001902685653001102704653003102715653002702746653000902773653001302782653003302795653004102828653002802869100002302897700001902920700002102939700002002960700001802980700002102998700001503019856004803034       2007                        eng d  a1053-811900aAn MEG-based brain-computer interface (BCI).0 aMEGbased braincomputer interface BCI  c07/2007  a581-930 v363 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Brain-computer interfaces (BCIs) allow for communicating intentions by mere brain activity, not involving muscles. Thus, BCIs may offer patients who have lost all voluntary muscle control the only possible way to communicate. Many recent studies have demonstrated that BCIs based on&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">electroencephalography</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">(</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">EEG</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">) can allow healthy and severely paralyzed individuals to communicate. While this approach is safe and inexpensive, communication is slow. Magnetoencephalography (MEG) provides signals with higher spatiotemporal resolution than&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">EEG</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;and could thus be used to explore whether these improved signal properties translate into increased BCI communication speed. In this study, we investigated the utility of an MEG-based BCI that uses voluntary amplitude modulation of sensorimotor mu and beta rhythms. To increase the signal-to-noise ratio, we present a simple spatial filtering method that takes the geometric properties of signal propagation in MEG into account, and we present methods that can process artifacts specifically encountered in an MEG-based BCI. Exemplarily, six participants were successfully trained to communicate binary decisions by imagery of limb movements using a feedback paradigm. Participants achieved significant mu rhythm self control within 32 min of feedback training. For a subgroup of three participants, we localized the origin of the amplitude modulated signal to the motor cortex. Our results suggest that an MEG-based BCI is feasible and efficient in terms of user training.</span></p>10aAdult10aAlgorithms10aArtifacts10aBrain10aElectroencephalography10aElectromagnetic Fields10aElectromyography10aFeedback10aFemale10aFoot10aHand10aHead Movements10aHumans10aMagnetic Resonance Imaging10aMagnetoencephalography10aMale10aMovement10aPrincipal Component Analysis10aSignal Processing, Computer-Assisted10aUser-Computer Interface1 aMellinger, Jürgen1 aSchalk, Gerwin1 aBraun, Christoph1 aPreissl, Hubert1 aRosenstiel, W1 aBirbaumer, Niels1 aKübler, A  uhttp://www.ncbi.nlm.nih.gov/pubmed/1747551101751nas a2200229   4500008004100000022001400041245012300055210006900178260001200247300001100259490000900270520101400279653001501293653002701308653001101335653003001346653001301376653004101389100001701430700002601447856004801473       2007                        eng d  a1557-170X00aNarrowband vs. broadband phase synchronization analysis applied to independent components of ictal and interictal EEG.0 aNarrowband vs broadband phase synchronization analysis applied t  c08/2007  a3864-70 v20073 aThis paper presents a comparison of the use of broadband and narrow band signals for phase synchronization analysis as applied to Independent Components of ictal and interictal scalp EEG in the context of seizure onset detection and prediction. Narrow band analysis for phase synchronization is found to be better performed in the present context than the broad band signal analysis. It has been observed that the phase synchronization of Independent Components in a narrow band (particularly the Gamma band) shows a prominent trend of increasing and decreasing synchronization at seizure onset near the epileptogenic area (spatially). This information is not always found to be consistent in analysis with the raw EEG signals, which may show spurious synchronization happening due to volume conduction effects. These observations lead us to believe that tracking changes in phase synchronization of narrow band activity, on continuous data records will be of great value in the context of seizure prediction.10aAlgorithms10aElectroencephalography10aHumans10aPredictive Value of Tests10aSeizures10aSignal Processing, Computer-Assisted1 aGupta, Disha1 aJames, Christopher, J  uhttp://www.ncbi.nlm.nih.gov/pubmed/1800284202708nas a2200373   4500008004100000022001400041245010900055210006900164260001200233300001100245490000600256520162400262653002201886653001201908653001001920653001801930653002501948653001501973653002901988653000902017653002402026653001702050653001202067653000902079653002502088653004102113653002602154100001602180700002302196700001902219700002802238700002002266856004802286       2006                        eng d  a1557-170X00aAnalysis of the correlation between local field potentials and neuronal firing rate in the motor cortex.0 aAnalysis of the correlation between local field potentials and n  c09/2006  a6185-80 v13 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Neuronal firing rate has been the signal of choice for invasive motor&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;machine&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">interfaces</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;(BMI). The use of local field potentials (LFP) in BMI experiments may provide additional dendritic information about movement intent and may improve performance. Here we study the time-varying amplitude modulated relationship between local field potentials (LFP) and single unit activity (SUA) in the motor cortex. We record LFP and SUA in the primary motor cortex of rats trained to perform a lever pressing task, and evaluate the correlation between pairs of peri-event time histograms (PETH) and movement evoked local field potentials (mEP) at the same electrode. Three different correlation coefficients were calculated and compared between the neuronal PETH and the magnitude and power of the mEP. Correlation as high as 0.7 for some neurons occurred between the PETH and the mEP magnitude. As expected, the correlations between the single trial LFP and SUV are much lower due to the inherent variability of both signals.</span></p>10aAction Potentials10aAnimals10aBrain10aBrain Mapping10aElectric Stimulation10aElectrodes10aEvoked Potentials, Motor10aMale10aModels, Statistical10aMotor Cortex10aNeurons10aRats10aRats, Sprague-Dawley10aSignal Processing, Computer-Assisted10aSynaptic Transmission1 aWang, Yiwen1 aSanchez, Justin, C1 aPrincipe, Jose1 aMitzelfelt, Jeremiah, D1 aGunduz, Aysegul  uhttp://www.ncbi.nlm.nih.gov/pubmed/1794674502917nas a2200265   4500008004100000022001400041245009700055210006900152260001200221300001000233490000800243520213200251653002202383653001202405653002102417653001302438653001802451653002102469653000902490653004102499100001902540700002302559700002102582856004802603       2002                        eng d  a0165-027000aTemporal transformation of multiunit activity improves identification of single motor units.0 aTemporal transformation of multiunit activity improves identific  c02/2002  a87-980 v1143 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">This report describes a temporally based method for identifying repetitive firing of motor units. This&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">approach</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;is ideally suited to spike trains with negative serially correlated inter-spike intervals (ISIs). It can also be applied to spike trains in which ISIs exhibit little serial correlation if their coefficient of variation (COV) is sufficiently low. Using a novel application of the Hough transform, this method (i.e. the modified Hough transform (MHT)) maps motor unit action potential (MUAP) firing times into a feature space with ISI and offset (defined as the latency from an arbitrary starting time to the first MUAP in the train) as dimensions. Each MUAP firing time corresponds to a pattern in the feature space that represents all possible MUAP trains with a firing at that time. Trains with stable ISIs produce clusters in the feature space, whereas randomly firing trains do not. The MHT provides a direct estimate of mean firing rate and its variability for the entire data segment, even if several individual MUAPs are obscured by firings from other motor units. Addition of this method to a shape-based classification&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">approach</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;markedly improved rejection of false positives using simulated data and identified spike trains in whole muscle electromyographic recordings from rats. The relative independence of the MHT from the need to correctly classify individual firings permits a global description of stable repetitive firing behavior that is complementary to shape-based approaches to MUAP classification.</span></p>10aAction Potentials10aAnimals10aElectromyography10aH-Reflex10aMotor Neurons10aMuscle, Skeletal10aRats10aSignal Processing, Computer-Assisted1 aSchalk, Gerwin1 aCarp, Jonathan, S.1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/1185004303091nas a2200373   4500008004100000022001400041245008600055210006900141260001200210300001100222490000600233520197800239653001502217653002002232653003602252653002102288653002702309653002202336653001102358653002702369653004102396653002802437100002102465700002102486700001902507700002602526700001702552700001902569700002102588700001802609700001802627700002402645856004802669       2000                        eng d  a1063-652800aBrain-computer interface technology: a review of the first international meeting.0 aBraincomputer interface technology a review of the first interna  c06/2000  a164-730 v83 a<p>Over the past decade, many laboratories have begun to explore brain-computer interface (BCI) technology as a radically new communication option for those with neuromuscular impairments that prevent them from using conventional augmentative communication methods. BCI's provide these users with communication channels that do not depend on peripheral nerves and muscles. This article summarizes the first international meeting devoted to BCI research and development. Current BCI's use electroencephalographic (EEG) activity recorded at the scalp or single-unit activity recorded from within cortex to control cursor movement, select letters or icons, or operate a neuroprosthesis. The central element in each BCI is a translation algorithm that converts electrophysiological input from the user into output that controls external devices. BCI operation depends on effective interaction between two adaptive controllers, the user who encodes his or her commands in the electrophysiological input provided to the BCI, and the BCI which recognizes the commands contained in the input and expresses them in device control. Current BCI's have maximum information transfer rates of 5-25 b/min. Achievement of greater speed and accuracy depends on improvements in signal processing, translation algorithms, and user training. These improvements depend on increased interdisciplinary cooperation between neuroscientists, engineers, computer programmers, psychologists, and rehabilitation specialists, and on adoption and widespread application of objective methods for evaluating alternative methods. The practical use of BCI technology depends on the development of appropriate applications, identification of appropriate user groups, and careful attention to the needs and desires of individual users. BCI research and development will also benefit from greater emphasis on peer-reviewed publications, and from adoption of standard venues for presentations and discussion.</p>10aAlgorithms10aCerebral Cortex10aCommunication Aids for Disabled10aDisabled Persons10aElectroencephalography10aEvoked Potentials10aHumans10aNeuromuscular Diseases10aSignal Processing, Computer-Assisted10aUser-Computer Interface1 aWolpaw, Jonathan1 aBirbaumer, Niels1 aHeetderks, W J1 aMcFarland, Dennis, J.1 aPeckham, P H1 aSchalk, Gerwin1 aDonchin, Emanuel1 aQuatrano, L A1 aRobinson, C J1 aVaughan, Theresa, M  uhttp://www.ncbi.nlm.nih.gov/pubmed/1089617802348nas a2200313   4500008004100000022001400041245009000055210006900145260000900214300001100223490001300234520141700247653001201664653003101676653002101707653002801728653002401756653002501780653001601805653004101821653001601862653002501878100001901903700001701922700001701939700001701956700001401973856004701987       1992                        eng d  a0022-073600aSpatiotemporal irregularities of spiral wave activity in isolated ventricular muscle.0 aSpatiotemporal irregularities of spiral wave activity in isolate  c1992  a113-220 v24 Suppl3 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Voltage-sensitive dyes and high resolution optical mapping were used to analyze the characteristics of spiral waves of excitation in isolated ventricular myocardium. In addition, analytical techniques, which have been previously used in the study of the characteristics of spiral waves in chemical reactions, were applied to determine the voltage structure of the center of the rotating activity (ie, the core). During stable spiral wave activity local activation occurs in a periodic fashion (ie, 1:1 stimulus: response activation ratio) throughout the preparation, except at the core, which is a small elongated area where the activity is of low voltage and the activation ratio is 1:0. The voltage amplitude increases gradually from the center of the core to the periphery. In some cases, however, regular activation patterns at the periphery may coexist with irregular local activation patterns near the core. Such a spatiotemporal irregularity is attended by variations in the core size and shape and results from changes in the core position. The authors conclude that functionally determined reentrant activity in the heart may be the result of spiral waves of propagation and that local spatiotemporal irregularities in the activation pattern are the result of changes in the core position.</span></p>10aAnimals10aCardiac Pacing, Artificial10aFluorescent Dyes10aHeart Conduction System10aMembrane Potentials10aOptics and Photonics10aPericardium10aSignal Processing, Computer-Assisted10aTachycardia10aVentricular Function1 aDavidenko, J M1 aPertsov, A V1 aSalomonsz, R1 aBaxter, Bill1 aJalife, J  uhttp://www.ncbi.nlm.nih.gov/pubmed/1552240