By Christoph Guger, Gernot Müller-Putz, Brendan Allison

This publication describes ten of the main promising brain-computer-interface (BCI) initiatives to have emerged in recent times. BCI learn is constructing quick, with many new principles, learn teams, and more desirable applied sciences. BCIs allow humans to speak simply by considering – with none circulation in any respect. numerous various teams have helped seriously disabled clients converse with BCIs, and BCI expertise can be being prolonged to facilitate restoration from stroke, epilepsy, and different conditions.

Each 12 months, enormous quantities of the head BCI scientists, engineers, medical professionals, and different visionaries compete for the main prestigious honor within the BCI examine group: the yearly BCI Award. The 2014 BCI Award pageant was once back aggressive, with sixty nine study teams vying for a nomination. This publication summarizes the 2014 BCI Award, together with the 10 tasks that have been nominated, the winner, and analyses and discussions of the submitted tasks and the way they mirror basic tendencies in BCI improvement. every one of those ten teams offers a bankruptcy summarizing their nominated venture, together with an advent, description of tools, effects, and more moderen paintings accomplished after the venture was once submitted. for that reason, this publication offers a state of the art evaluate of the latest BCI learn tendencies, from best teams, in a simple to learn layout with a number of assisting images, graphs, and figures.

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1088/1741-2560/9/3/036013 F. S. Brumberg, P. Brunner, A. L. Ritaccio, C. Guan, G. Schalk, Front Hum Neurosci 9, 97 (2015). 00097 S. Martin, P. Brunner, C. J. E. Crone, J. Rieger, G. T. Knight, B. Pasley, Front Neuroeng 7(14) (2014). 00014 J. Mellinger, G. Schalk, in Toward Brain-Computer Interfacing, ed. by G. Dornhege, J. del R. Millan, T. Hinterberger, D. McFarland, K. Müller (MIT Press, Cambridge, 2007), pp. N. V. David, N. Mesgarani, A. A. E. T. F. Chang, PLoS Biol. 10(1), e1001251 (2012). 1001251 X.

For this, we applied an 18th order 70–170 Hz Butterworth filter and then extracted the envelope of the filtered signals using the Hilbert transform. Finally, we low-pass filtered the resulting signal envelope at 6 Hz (anti-aliasing) and downsampled the result to 120 Hz. , the envelope of the signal waveform in the speech band. To do this, we applied a 80–6000 Hz Butterworth filter to each audio signal, and then extracted the envelope of the filtered signals using the Hilbert transform. Finally, we low-pass filtered the speech envelopes at 6 Hz and downsampled them to 120 Hz.

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. Furthermore, it appeared to be an efficient strategy, since scores in several cognitive functions increased after only five sessions. A similar study that did not use MI-BCI needed more than 30 sessions to achieve statistically significant increments in the measured cognitive functions (Wang and Hsieh 2013).

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