biomimetic interfaces
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2021 ◽  
Vol Volume 14 ◽  
pp. 7-27
Author(s):  
Eduarda Fernandes ◽  
Sofia Benfeito ◽  
Fernando Cagide ◽  
Hugo Gonçalves ◽  
Sigrid Bernstorff ◽  
...  

2019 ◽  
Vol 16 ◽  
pp. 456-473 ◽  
Author(s):  
Behnam Akhavan ◽  
Michiel Croes ◽  
Steven G. Wise ◽  
Chongpu Zhai ◽  
Juichien Hung ◽  
...  

2019 ◽  
Vol 6 ◽  
Author(s):  
Saziye Yorulmaz Avsar ◽  
Myrto Kyropoulou ◽  
Stefano Di Leone ◽  
Cora-Ann Schoenenberger ◽  
Wolfgang P. Meier ◽  
...  

2018 ◽  
Vol 10 (2) ◽  
pp. 129-137 ◽  
Author(s):  
Tobias Pfeiffer ◽  
Antonio De Nicola ◽  
Costanza Montis ◽  
Francesco Carlà ◽  
Nico F. A. van der Vegt ◽  
...  

2018 ◽  
Vol 30 (5) ◽  
pp. 1323-1358 ◽  
Author(s):  
Yin Zhang ◽  
Steve M. Chase

Brain-computer interfaces are in the process of moving from the laboratory to the clinic. These devices act by reading neural activity and using it to directly control a device, such as a cursor on a computer screen. An open question in the field is how to map neural activity to device movement in order to achieve the most proficient control. This question is complicated by the fact that learning, especially the long-term skill learning that accompanies weeks of practice, can allow subjects to improve performance over time. Typical approaches to this problem attempt to maximize the biomimetic properties of the device in order to limit the need for extensive training. However, it is unclear if this approach would ultimately be superior to performance that might be achieved with a nonbiomimetic device once the subject has engaged in extended practice and learned how to use it. Here we approach this problem using ideas from optimal control theory. Under the assumption that the brain acts as an optimal controller, we present a formal definition of the usability of a device and show that the optimal postlearning mapping can be written as the solution of a constrained optimization problem. We then derive the optimal mappings for particular cases common to most brain-computer interfaces. Our results suggest that the common approach of creating biomimetic interfaces may not be optimal when learning is taken into account. More broadly, our method provides a blueprint for optimal device design in general control-theoretic contexts.


ACS Omega ◽  
2018 ◽  
Vol 3 (4) ◽  
pp. 3882-3891 ◽  
Author(s):  
Beatrix Peter ◽  
Rita Ungai-Salanki ◽  
Bálint Szabó ◽  
Agoston G. Nagy ◽  
Inna Szekacs ◽  
...  

2018 ◽  
Vol 20 (14) ◽  
pp. 9328-9336 ◽  
Author(s):  
Giuseppe Licari ◽  
Joseph S. Beckwith ◽  
Saeideh Soleimanpour ◽  
Stefan Matile ◽  
Eric Vauthey

A mechanosensitive harmonophore is used to probe the order and lateral pressure in phospholipid monolayers by surface-second harmonic generation.


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