03153nas 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

Brain-computer interfaces (BCIs) can be used for communication in writing without muscular activity or for learning to control seizures by voluntary regulation of brain 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 computer-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.

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/1849554103358nas a2200397 4500008004100000020001800041245009600059210006900155260015500224520204800379653003002427653000802457653001502465653003002480653002302510653002902533653003102562100002002593700001502613700001502628700001302643700001902656700001902675700001402694700001302708700002502721700002302746700001402769700002402783700001502807700002102822700001802843700001802861700002102879856006002900 2007 eng d a978026225604900aBrain Computer Interfaces for Communication in Paralysis: a Clinical-Experimental Approach.0 aBrain Computer Interfaces for Communication in Paralysis a Clini bVirtual Library of Psychology at Saarland University and State Library, GERMANY, PsyDok [http://psydok.sulb.uni-saarland.de/phpoai/oai2.php] (Germany)3 a

An overview of different approaches to brain-computer interfaces (BCIs) developed in our laboratory is given. An important clinical application of BCIs is to enable communication or environmental control in severely paralyzed patients. The BCI “Thought-Translation Device (TTD)” allows verbal communication through the voluntary self-regulation of brain signals (e.g., slow cortical potentials (SCPs)), which is achieved by operant feedback training. Humans' ability to self-regulate their SCPs is used to move a cursor toward a target that contains a selectable letter set. Two different approaches were followed to developWeb browsers that could be controlled with binary brain responses. Implementing more powerful classification methods including different signal parameters such as oscillatory features improved our BCI considerably. It was also tested on signals with implanted electrodes. Most BCIs provide the user with a visual feedback interface. Visually impaired patients require an auditory feedback mode. A procedure using auditory (sonified) feedback of multiple EEG parameters was evaluated. Properties of the auditory systems are reported and the results of two experiments with auditory feedback are presented. Clinical data of eight ALS patients demonstrated that all patients were able to acquire efficient brain control of one of the three available BCI systems (SCP, µ-rhythm, and P300), most of them used the SCP-BCI. A controlled comparison of the three systems in a group of ALS patients, however, showed that P300-BCI and the µ-BCI are faster and more easily acquired than SCP-BCI, at least in patients with some rudimentary motor control left. Six patients who started BCI training after entering the completely locked-in state did not achieve reliable communication skills with any BCI system. One completely locked-in patient was able t o communicate shortly with a ph-meter, but lost control afterward.

10abrain-computer interfaces10aEEG10aexperiment10aMedical sciences Medicine10aparalyzed patients10aslow cortical potentials10aThought-Translation Device1 aHinterberger, T1 aNijboer, F1 aKübler, A1 aMatuz, T1 aFurdea, Adrian1 aMochty, Ursula1 aJordan, M1 aLal, T N1 aHill, Jeremy, Jeremy1 aMellinger, Jürgen1 aBensch, M1 aTangermann, Michael1 aWidmann, G1 aElger, Christian1 aRosenstiel, W1 aSchölkopf, B1 aBirbaumer, Niels uhttp://psydok.sulb.uni-saarland.de/volltexte/2008/2154/02785nas a2200445 4500008004100000022001400041245015300055210006900208260001200277300001000289490000700299520147600306653001501782653002801797653002101825653002701846653002701873653002201900653001101922653001101933653001601944653000901960653001601969653001401985653003501999653002802034100002502062700001302087700002302100700002002123700002102143700001502164700001902179700001802198700002102216700001802237700001502255700002102270856004802291 2006 eng d a1534-432000aClassifying EEG and ECoG signals without subject training for fast BCI implementation: comparison of nonparalyzed and completely paralyzed subjects.0 aClassifying EEG and ECoG signals without subject training for fa c06/2006 a183-60 v143 a

We summarize results from a series of related studies that aim to develop a motor-imagery-based brain-computer interface using a single recording session of electroencephalogram (EEG) or electrocorticogram (ECoG) signals for each subject. We apply the same experimental and analytical methods to 11 nonparalysed subjects (eight EEG, three ECoG), and to five paralyzed subjects (four EEG, one ECoG) who had been unable to communicate for some time. While it was relatively easy to obtain classifiable signals quickly from most of the nonparalyzed subjects, it proved impossible to classify the signals obtained from the paralyzed patients by the same methods. This highlights the fact that though certain BCI paradigms may work well with healthy subjects, this does not necessarily indicate success with the target user group. We outline possible reasons for this failure to transfer.

10aAlgorithms10aArtificial Intelligence10aCluster Analysis10aComputer User Training10aElectroencephalography10aEvoked Potentials10aFemale10aHumans10aImagination10aMale10aMiddle Aged10aParalysis10aPattern Recognition, Automated10aUser-Computer Interface1 aHill, Jeremy, Jeremy1 aLal, T N1 aSchröder, Michael1 aHinterberger, T1 aWilhelm, Barbara1 aNijboer, F1 aMochty, Ursula1 aWidman, Guido1 aElger, Christian1 aSchölkopf, B1 aKübler, A1 aBirbaumer, Niels uhttp://www.ncbi.nlm.nih.gov/pubmed/1679228901905nas a2200277 4500008004100000020002200041245007800063210006900141260003300210300001200243490000900255520107600264100002501340700001301365700002301378700002001401700001801421700002101439700001801460700002101478700001901499700002601518700002201544700001901566856004201585 2006 eng d a978-3-540-44412-100aClassifying Event-Related Desynchronization in EEG, ECoG and MEG Signals.0 aClassifying EventRelated Desynchronization in EEG ECoG and MEG S bSpringer Berlin / Heidelberg a404-4130 v41743 a

We employed three different brain signal recording methods to perform Brain-Computer Interface studies on untrained subjects. In all cases, we aim to develop a system that could be used for fast, reliable preliminary screening in clinical BCI application, and we are interested in knowing how long screening sessions need to be. Good performance could be achieved, on average, after the first 200 trials in EEG, 75–100 trials in MEG, or 25–50 trials in ECoG. We compare the performance of Independent Component Analysis and the Common Spatial Pattern algorithm in each of the three sensor types, finding that spatial filtering does not help in MEG, helps a little in ECoG, and improves performance a great deal in EEG. In all cases the unsupervised ICA algorithm performed at least as well as the supervised CSP algorithm, which can suffer from poor generalization performance due to overfitting, particularly in ECoG and MEG.

1 aHill, Jeremy, Jeremy1 aLal, T N1 aSchröder, Michael1 aHinterberger, T1 aWidman, Guido1 aElger, Christian1 aSchölkopf, B1 aBirbaumer, Niels1 aFranke, Katrin1 aMüller, Klaus-Robert1 aNickolay, Bertram1 aSchäfer, Ralf uhttp://dx.doi.org/10.1007/11861898_4101333nas a2200145 4500008004100000245004900041210004800090260001200138520093100150100002001081700002501101700001301126700001801139856003001157 2006 eng d00aRegularised CSP for Sensor Selection in BCI.0 aRegularised CSP for Sensor Selection in BCI c01/20063 a

The Common Spatial Pattern (CSP) algorithm is a highly successful method for efficiently calculating spatial filters for brain signal classification. Spatial filtering can improve classification performance considerably, but demands that a large number of electrodes be mounted, which is inconvenient in day-to-day BCI usage. The CSP algorithm is also known for its tendency to overfit, i.e. to learn the noise in the training set rather than the signal. Both problems motivate an approach in which spatial filters are sparsified. We briefly sketch a reformulation of the problem which allows us to do this, using 1-norm regularisation. Focusing on the electrode selection issue, we present preliminary results on EEG data sets that suggest that effective spatial filters may be computed with as few as 10–20 electrodes, hence offering the potential to simplify the practical realisation of BCI systems significantly.

1 aFarquhar, Jason1 aHill, Jeremy, Jeremy1 aLal, T N1 aSchölkopf, B uhttp://edoc.mpg.de/31206001035nas a2200145 4500008004100000245005800041210005500099260001200154520061700166100002500783700002000808700001300828700001800841856003000859 2006 eng d00aTime-Dependent Demixing of Task-Relevant EEG Signals.0 aTimeDependent Demixing of TaskRelevant EEG Signals c09/20063 a

Given a spatial filtering algorithm that has allowed us to identify task-relevant EEG sources, we present a simple approach for monitoring the activity of these sources while remaining relatively robust to changes in other (task-irrelevant) brain activity. The idea is to keep spatial *patterns* fixed rather than spatial filters, when transferring from training to test sessions or from one time window to another. We show that a fixed spatial pattern (FSP) approach, using a moving-window estimate of signal covariances, can be more robust to non-stationarity than a fixed spatial filter (FSF) approach.

1 aHill, Jeremy, Jeremy1 aFarquhar, Jason1 aLal, T N1 aSchölkopf, B uhttp://edoc.mpg.de/31205301846nas a2200241 4500008004100000245008500041210006900126260001200195520097600207653003001183653005001213100001301263700002301276700002501299700002001324700002001344700002301364700001901387700001801406700002101424700002501445856013401470 2005 eng d00aA Brain Computer Interface with Online Feedback based on Magnetoencephalography.0 aBrain Computer Interface with Online Feedback based on Magnetoen c08/20053 a

The aim of this paper is to show that machine learning techniques can be used to derive a classifying function for human brain signal data measured by magnetoencephalography (MEG), for the use in a brain computer interface (BCI). This is especially helpful for evaluating quickly whether a BCI approach based on electroencephalography, on which training may be slower due to lower signalto-noise ratio, is likely to succeed. We apply RCE and regularized SVMs to the experimental data of ten healthy subjects performing a motor imagery task. Four subjects were able to use a trained classifier to write a short name. Further analysis gives evidence that the proposed imagination task is suboptimal for the possible extension to a multiclass interface. To the best of our knowledge this paper is the first working online MEG-based BCI and is therefore a “proof of concept”.

10aBrain Computer Interfaces10aUser Modelling for Computer Human Interaction1 aLal, T N1 aSchroeder, Michael1 aHill, Jeremy, Jeremy1 aPreissl, Hubert1 aHinterberger, T1 aMellinger, Jürgen1 aBogdan, Martin1 aRosenstiel, W1 aBirbaumer, Niels1 aSchoelkopf, Bernhard uhttp://www.researchgate.net/publication/221346004_A_brain_computer_interface_with_online_feedback_based_on_magnetoencephalography01807nas a2200229 4500008004100000020001800041245006200059210006100121260000900182520111100191653003001302100001301332700002001345700001801365700002301383700002501406700001801431700002101449700001801470700002101488856006801509 2005 eng d a0-262-19534-800aMethods Towards Invasive Human Brain Computer Interfaces.0 aMethods Towards Invasive Human Brain Computer Interfaces c20053 a

During the last ten years there has been growing interest in the develop- ment of Brain Computer Interfaces (BCIs). The field has mainly been driven by the needs of completely paralyzed patients to communicate. With a few exceptions, most human BCIs are based on extracranial elec- troencephalography (EEG). However, reported bit rates are still low. One reason for this is the low signal-to-noise ratio of the EEG [16]. We are currently investigating if BCIs based on electrocorticography (ECoG) are a viable alternative. In this paper we present the method and examples of intracranial EEG recordings of three epilepsy patients with electrode grids placed on the motor cortex. The patients were asked to repeat- edly imagine movements of two kinds, e.g., tongue or finger movements. We analyze the classifiability of the data using Support Vector Machines (SVMs) [18, 21] and Recursive Channel Elimination (RCE) [11]. 

10aBrain Computer Interfaces1 aLal, T N1 aHinterberger, T1 aWidman, Guido1 aSchroeder, Michael1 aHill, Jeremy, Jeremy1 aRosenstiel, W1 aElger, Christian1 aSchölkopf, B1 aBirbaumer, Niels uhttp://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.64.848602144nas a2200301 4500008004100000245008000041210006900121260001200190300001600202490000900218520113600227653002901363653002201392653002701414653002201441653003401463653002701497100002301524700001301547700002001560700001901580700002501599700002101624700001801645700001801663700003301681856012801714 2005 eng d00aRobust EEG Channel Selection across Subjects for Brain-Computer Interfaces.0 aRobust EEG Channel Selection across Subjects for BrainComputer I c01/2005 a3103–31120 v20053 a

Most EEG-based brain-computer interface (BCI) paradigms come along with specific electrode positions, for example, for a visual-based BCI, electrode positions close to the primary visual cortex are used. For new BCI paradigms it is usually not known where task relevant activity can be measured from the scalp. For individual subjects, Lal et al. in 2004 showed that recording positions can be found without the use of prior knowledge about the paradigm used. However it remains unclear to what extent their method of recursive channel elimination (RCE) can be generalized across subjects. In this paper we transfer channel rankings from a group of subjects to a new subject. For motor imagery tasks the results are promising, although cross-subject channel selection does not quite achieve the performance of channel selection on data of single subjects. Although the RCE method was not provided with prior knowledge about the mental task, channels that are well known to be important (from a physiological point of view) were consistently selected whereas task-irrelevant channels were reliably disregarded.

10abrain-computer interface10achannel selection10aElectroencephalography10afeature selection10arecursive channel elimination10asupport vector machine1 aSchröder, Michael1 aLal, T N1 aHinterberger, T1 aBogdan, Martin1 aHill, Jeremy, Jeremy1 aBirbaumer, Niels1 aRosenstiel, W1 aSchölkopf, B1 aVesin J M, EbrahimiEditor, T uhttp://www.researchgate.net/publication/26532072_Robust_EEG_Channel_Selection_across_Subjects_for_Brain-Computer_Interfaces01486nas a2200169 4500008004100000245005800041210005400099260000900153520094500162653003001107100002501137700001301162700001401175700002101189700001801210856008801228 2004 eng d00aAn Auditory Paradigm for Brain–Computer Interfaces.0 aAuditory Paradigm for Brain–Computer Interfaces c20043 a

 

Motivated by the particular problems involved in communicating with “locked-in” paralysed patients, we aim to develop a brain- computer interface that uses auditory stimuli. We describe a paradigm that allows a user to make a binary decision by focusing attention on one of two concurrent auditory stimulus sequences. Using Support Vector Machine classification and Recursive Chan- nel Elimination on the independent components of averaged event- related potentials, we show that an untrained user’s EEG data can be classified with an encouragingly high level of accuracy. This suggests that it is possible for users to modulate EEG signals in a single trial by the conscious direction of attention, well enough to be useful in BCI. 

10aBrain Computer Interfaces1 aHill, Jeremy, Jeremy1 aLal, T N1 aBierig, K1 aBirbaumer, Niels1 aSchölkopf, B uhttp://papers.nips.cc/paper/2551-an-auditory-paradigm-for-brain-computer-interfaces