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Brain-Computer Interface Research: A State-of-the-Art by Christoph Guger, Brendan Allison, Günter Edlinger (auth.),

By Christoph Guger, Brendan Allison, Günter Edlinger (auth.), Christoph Guger, Brendan Z. Allison, Günter Edlinger (eds.)

Brain-computer interfaces (BCIs) are swiftly constructing right into a mainstream, all over the world examine recreation. With such a lot of new teams and tasks, it may be tricky to spot the simplest ones. This ebook summarizes ten major tasks from worldwide. approximately 60 submissions have been bought in 2011 for the hugely aggressive BCI learn Award, and a global jury chosen the pinnacle ten. This short supplies a concise yet conscientiously illustrated and completely up to date description of every of those initiatives, including an advent and concluding bankruptcy via the editors.

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Detecting Movement Intention for Stroke Rehabilitation 31 Fig. 3 Changes of hemodynamic and physiological parameters during a finger pinching task compared to rest. a, b hemodynamic response in M1 and PMC, respectively. c–f physiological parameters. Error bars indicate standard error of the mean, * indicate significance on the 5 %level (paired t-test). Ó 2011 IEEE. Reprinted, with permission, from Zimmermann et al. (2011) Discussion The above results lead to the conclusion that the proposed system is capable of simultaneously measuring meaningful data from the motor cortices as well as from the body during a pinching task.

2006; Shenoy et al. 2006; Sugiyama et al. 2007). It is also conceivable, however, that such modulatory effects do not induce a shift in the distributions of l-power, but rather alter their variance. This situation is depicted in Fig. 3. Here, the optimal decision boundary for different activity levels of PFC remains identical. Strong activation of PFC, however, leads to a smaller overlap between the distributions of l-power at rest and during motorimagery, resulting in a smaller minimum Bayes error (as indicated by the overlap of the two distributions shown in dark gray).

In a pilot study, we trained three healthy subjects to modulate fronto-parietal c-power by means of neurofeedback based on online beamforming (Grosse-Wentrup 2011a). Two of the three subjects displayed statistically significant control of c-power after one and three training sessions, respectively. As hypothesized, volitional attenuation of fronto-parietal c-power was accompanied by a statistically significant increase in l-power over sensorimotor cortex. These results indicate that subjects can learn how to generate a strong SMR by regulating fronto-parietal c-power, thereby achieving a state-ofmind known to positively correlate with BCI-performance (Blankertz et al.

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