By Christoph Guger, Gernot Müller-Putz, Brendan Allison
This e-book describes ten of the main promising brain-computer-interface (BCI) tasks to have emerged lately. BCI study is constructing quick, with many new rules, examine teams, and better applied sciences. BCIs allow humans to speak simply by pondering – with none stream in any respect. a number of varied teams have helped seriously disabled clients speak with BCIs, and BCI know-how can be being prolonged to facilitate restoration from stroke, epilepsy, and different conditions.
Each yr, 1000s of the head BCI scientists, engineers, medical professionals, and different visionaries compete for the main prestigious honor within the BCI study neighborhood: the yearly BCI Award. The 2014 BCI Award pageant was once back aggressive, with sixty nine learn teams vying for a nomination. This e-book summarizes the 2014 BCI Award, together with the 10 initiatives that have been nominated, the winner, and analyses and discussions of the submitted tasks and the way they replicate basic developments in BCI improvement. every one of those ten teams offers a bankruptcy summarizing their nominated undertaking, together with an advent, description of tools, effects, and more recent paintings accomplished after the undertaking used to be submitted. therefore, this ebook presents a state of the art review of the most recent BCI study developments, from most sensible teams, in a simple to learn layout with a number of assisting photographs, graphs, and figures.
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Additional info for Brain-Computer Interface Research: A State-of-the-Art Summary 4
Two of the most widely used methods to stimulate certain frequencies or brain regions in NFT-related studies are evoking emotions or presenting emotional faces (Güntekin and Basar 2007). Although motor imagery strategies were also used for neurofeedback approaches in different diseases as autism (Pineda et al. 2014), MI-BCI is relatively novel to evaluate cognitive changes with elderly users. By means of the use of a BCI, a friendly environment is presented to the real users. Hence, participants were more predisposed to perform the training.
Phonemes, syllables, words and phrases) are ﬁrst decoded from brain signals and then synthesized into speech. While recent studies have demonstrated this possibility (Pei et al. 2011, 2012; Leuthardt et al. 2011; Martin et al. 2014; Lotte et al. , ECoG, LFPs, single neuronal recordings) are currently unable to capture the entire complexity of expressive speech. Consequently, silent speech BCIs are limited in the vocabulary that can be decoded directly from the brain signals. The second avenue is to replace unnatural stimuli that require an artiﬁcial mapping with speech stimuli that do not.
Figure 5 shows screenshots of each of the three stages of this task. Fig. 3 Screenshots of the interface during T3. The top panels represent feedback during right hand motor imagery. The bottom panels reflect left hand motor imagery feedback Neurofeedback Training with a Motor Imagery-Based BCI … 49 Fig. 4 Screenshots of the interface during T4. Users must overcome obstacles using hand motor imagery Fig. 5 Screenshots of the interface during T5. During the ﬁrst stage (left), users had to memorize these two items.