By Christoph Guger, Brendan Allison, Junichi Ushiba

This publication stories at the most recent learn and advancements within the box of brain-computer interfaces (BCIs). It introduces ten amazing and cutting edge BCI initiatives, nominated as finalists for the BCI award 2012 by way of a jury of validated researchers and discusses how all of the nominated initiatives displays common world wide tendencies in BCI improvement. on the center of the booklet, 9 of those ten initiatives are defined in designated person chapters. those comprise a targeted advent to every venture, a simple to know description of the tools and a well timed document at the most up-to-date advancements accomplished because the submission to the award. consequently, this publication offers a state of the art evaluate of the most recent BCI study developments, from major specialists, in a simple to learn layout supported through explanatory photos, graphs and figures.

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Finally, it would also be interesting to evaluate the role of BCI-based adaptation in real applications such as medical training systems notably in terms of learning performance. Indeed, activated assistance only when user presented a high mental workload could improve the learning performance compared to assistance activated all the time. 26 6 L. George et al. Conclusion In this work, we studied the combination of haptic interfaces and Brain-Computer Interfaces (BCI). We proposed to use BCI technology to adapt a haptic guidance system according to a mental workload index.

E. 10 pulses. Reading Visual Braille with a Retinal Prosthesis 33 To test the subject’s ability to read words in visual braille, the 10 most common 2-, 3-, and 4-letter words in French (the native language of the subject, Table 1) were picked based on usage frequency [6,7]. Each word was presented with 500ms per letter and 1000ms break between letters. The subject was informed that short words would be presented, but was not aware of which words were contained in the set. The order of the words was random and each word was stimulated once.

Observe that, although the SNR is greatest at the occipital region [upper], the response can be clearly detected using Ear-EEG [lower]. 1 47 BCI Performance Evaluation For rigor we evaluated the BCI performance of Ear-EEG using a metric that is based on the estimated SSVEP SNR and independent of the stimulus presentation. This was done as stimulus design is itself a focus of research and can greatly affect the performance of a BCI system. The Shannon-Hartley theorem, C = B log2 (1 + SNR) (1) where B is the bandwidth of the channel and the SNR is expressed as a linear power ratio, was used to evaluate the bitrate performances (bits/sec) for each electrode.

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