02228nas a2200289 4500008004100000022001400041245012100055210006900176260001200245300001100257490000900268520131200277653001501589653001401604653003301618653002701651653001301678653002901691653001701720653003101737653003201768653002801800100002001828700002301848700001901871856004801890 2008 eng d a1557-170X00aElectrocorticographic interictal spike removal via denoising source separation for improved neuroprosthesis control.0 aElectrocorticographic interictal spike removal via denoising sou c08/2008 a5224-70 v20083 a

Electrocorticographic (ECoG) neuroprosthesis is a promising area of research that could provide channels of communication and control for patients who have lost their motor functions due to damage to the nervous system. However, implantation of subdural electrodes are clinically restricted to diagnostics of pre-surgical epileptic patients. Hence, interictal activity is present in the recordings across various areas of the sensorimotor cortex and suppresses the amplitude modulated features extracted to model hand trajectories. Denoising source separation is a recently introduced framework which extracts hidden structures of interest within the data through denoising the source estimates with filters designed around prior knowledge on the observations. Herein, we exploit the high amplitude quasiperiodic nature of the observed interictal spikes and show that removal of the interictal activity improves linear prediction of hand trajectories.

10aAlgorithms10aArtifacts10aDiagnosis, Computer-Assisted10aElectroencephalography10aEpilepsy10aEvoked Potentials, Motor10aMotor Cortex10aReproducibility of Results10aSensitivity and Specificity10aUser-Computer Interface1 aGunduz, Aysegul1 aSanchez, Justin, C1 aPrincipe, Jose uhttp://www.ncbi.nlm.nih.gov/pubmed/1916389503540nas a2200457 4500008004100000022001400041245004900055210004100104260001200145300001100157490000700168520234400175653001002519653001502529653001402544653001002558653002702568653002702595653002102622653001302643653001102656653000902667653000902676653001902685653001102704653003102715653002702746653000902773653001302782653003302795653004102828653002802869100002302897700001902920700002102939700002002960700001802980700002102998700001503019856004803034 2007 eng d a1053-811900aAn MEG-based brain-computer interface (BCI).0 aMEGbased braincomputer interface BCI c07/2007 a581-930 v363 a

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 electroencephalography(EEG) 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 EEG 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.

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/1747551103064nas a2200373 4500008004100000022001400041245009000055210006900145260001200214300001000226490000700236520200500243653002902248653001002277653001502287653001402302653001002316653001802326653002702344653003002371653001302401653001102414653001602425653002802441653001302469653002002482653002802502653002202530100002102552700002602573700002402599700001902623856004802642 2003 eng d a1534-432000aThe Wadsworth Center brain-computer interface (BCI) research and development program.0 aWadsworth Center braincomputer interface BCI research and develo c06/2003 a204-70 v113 a

Brain-computer interface (BCI) research at the Wadsworth Center has focused primarily on using electroencephalogram (EEG) rhythms recorded from the scalp over sensorimotor cortex to control cursor movement in one or two dimensions. Recent and current studies seek to improve the speed and accuracy of this control by improving the selection of signal features and their translation into device commands, by incorporating additional signal features, and by optimizing the adaptive interaction between the user and system. In addition, to facilitate the evaluation, comparison, and combination of alternative BCI methods, we have developed a general-purpose BCI system called BCI-2000 and have made it available to other research groups. Finally, in collaboration with several other groups, we are developing simple BCI applications and are testing their practicality and long-term value for people with severe motor disabilities.

10aAcademic Medical Centers10aAdult10aAlgorithms10aArtifacts10aBrain10aBrain Mapping10aElectroencephalography10aEvoked Potentials, Visual10aFeedback10aHumans10aMiddle Aged10aNervous System Diseases10aResearch10aResearch Design10aUser-Computer Interface10aVisual Perception1 aWolpaw, Jonathan1 aMcFarland, Dennis, J.1 aVaughan, Theresa, M1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/12899275