By Christoph Guger, Brendan Z. Allison, Günter Edlinger
Brain-computer interfaces (BCIs) are swiftly constructing right into a mainstream, around the globe learn recreation. With such a lot of new teams and initiatives, it may be tough to spot the easiest ones. This booklet summarizes ten best initiatives from all over the world. approximately 60 submissions have been got in 2011 for the hugely aggressive BCI learn Award, and a world jury chosen the pinnacle ten. This short provides a concise yet rigorously illustrated and completely updated description of every of those initiatives, including an advent and concluding bankruptcy by way of the editors.
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E. multiple simultaneously acquired measurement data. Training samples were used to individually find the most informative feature signals from the fNIRS data, and to train two HMMs separately, one HMM for data where the subject was at rest and one with data where she/he was pinching. Two cases were investigated: first, the observations were limited to fNIRS data only, and second, the observation space was extended by the physiological signals. The classification of a single signal segment that belonged to the test data set was carried out by considering the two trained HMMs as generative models and comparing the likelihood that either model produced the observed signal.
Eng. 56(4), 1209–1219 (2009) M. Grosse-Wentrup, B. Schölkopf, J. Hill, Causal influence of gamma oscillations on the sensorimotor rhythm. NeuroImage 56(2), 837–842 (2011) M. Grosse-Wentrup, B. Schölkopf, High gamma-power predicts performance in sensorimotorrhythm brain-computer interfaces. J. Neural Eng. 9(4), 046001 (2012) C. Guger, G. Edlinger, W. Harkam, I. Niedermayer, G.
Wolf, R. Gassert, Towards a BCI for sensorimotor training: initial results from simultaneous fNIRS and biosignal recordings, in Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2011 (EMBC 2011), pp. 6339–6343 (2011) R. Zimmermann, L. Marchal-Crespo, J. Edelmann, O. C. Fluet, R. Riener, M. Wolf, R. Gassert, Detection of motor execution using a hybrid fNIRS-biosignal BCI: a feasibility study. J. NeuroEng. Rehabil. C. Metzger, O. Lambercy, D. Chapuis, R. Gassert, Design and characterization of the ReHapticKnob, a robot for assessment and therapy of hand function, in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2011, 25–30 Sept 2011, pp.