03540nas 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/1747551102315nas a2200241 4500008004100000022001400041245006000055210005800115260001200173300001400185490000800199520163200207653002101839653002501860653001701885653001601902100002301918700002301941700002101964700001901985700002102004856004802025 2005 eng d a0165-027000aLong-term spinal reflex studies in awake behaving mice.0 aLongterm spinal reflex studies in awake behaving mice c12/2005 a134–1430 v1493 aThe increasing availability of genetic variants of mice has facilitated studies of the roles of specific molecules in specific behaviors. The contributions of such studies could be strengthened and extended by correlation with detailed information on the patterns of motor commands throughout the course of specific behaviors in freely moving animals. Previously reported methodologies for long-term recording of electromyographic activity (EMG) in mice using implanted electrodes were designed for intermittent, but not continuous operation. This report describes the fabrication, implantation, and utilization of fine wire electrodes for continuous long-term recordings of spontaneous and nerve-evoked EMG in mice. Six mice were implanted with a tibial nerve cuff electrode and EMG electrodes in soleus and gastrocnemius muscles. Wires exited through a skin button and traveled through an armored cable to an electrical commutator. In mice implanted for 59-144 days, ongoing EMG was monitored continuously (i.e., 24 h/day, 7 days/week) by computer for 18-92 days (total intermittent recording for 25-130 days). When the ongoing EMG criteria were met, the computer applied the nerve stimulus, recorded the evoked EMG response, and determined the size of the M-response (MR) and the H-reflex (HR). It continually adjusted stimulation intensity to maintain a stable MR size. Stable recordings of ongoing EMG, MR, and HR were obtained typically 3 weeks after implantation. This study demonstrates the feasibility of long-term continuous EMG recordings in mice for addressing a variety of neurophysiological and behavioral issues.10aElectromyography10aimplanted electrodes10aMonosynaptic10aSpinal Cord1 aCarp, Jonathan, S.1 aTennissen, Ann, M.1 aChen, Xiang Yang1 aSchalk, Gerwin1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/1602684802917nas 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

This report describes a temporally based method for identifying repetitive firing of motor units. This approach 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 approach 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.

10aAction Potentials10aAnimals10aElectromyography10aH-Reflex10aMotor Neurons10aMuscle, Skeletal10aRats10aSignal Processing, Computer-Assisted1 aSchalk, Gerwin1 aCarp, Jonathan, S.1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/11850043