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<p><b>Title</b>: Advances in Principles, Methods and Applications of
Brain-Computer Interaction<b><br>
</b></p>
<p><b>Website</b>: <a class="moz-txt-link-freetext" href="https://urldefense.com/v3/__https://www.mdpi.com/si/161595__;!!HJOPV4FYYWzcc1jazlU!70JhP0kOsRX2RjgmDb3pZJsYlPAR2Opu9lespTH94psmvO_bqKn5qdE0_KC7zMXWk8YBqv3c-argDlMOFfqi6onmjDGyp8F4Kp8R3XSO$">https://www.mdpi.com/si/161595</a></p>
<p><b>Special Issue Information:</b><br>
<br>
Brain–computer interfaces (BCIs) represent a continuously growing
research field that originated in an attempt to enable subjects
with severe neuromuscular disorders to communicate and interact
with the world around them. Advances in the capabilities of
sensors, computation devices, and wireless technologies, as well
as in signal processing, machine learning and neuroscience methods
have expanded the BCI concept, and it is now subject to
investigation in a wide range of fields such as remote healthcare,
industry, marketing, education, and gaming. Recently, the use of
BCI technology in other aspects of daily life, including mental
load management, decision making, neuro-marketing, and gaming, has
been explored. As the aspiration is that BCI technology will
gradually move towards use in practical applications, the need for
more reliable and robust solutions for detecting user intent is,
in the current landscape, as urgent and important as it ever has
been. The battle to deploy BCI technology in real-world settings
is fought on multiple fronts. Novel neural interface and other
hardware devices promise to improve the signal-to-noise rate of
brain signals and user acceptance. Continued efforts in signal
processing and artificial intelligence are enhancing the decoding
capabilities of BCIs. New developments in the design principles of
BCI systems, such as shared-control, hybrid BCI and co-adaptive
user training are finding use in attempts to widen user access to
BCI apparatuses. Additionally, increasing the user evaluation of
established and novel BCI applications is broadening the scope of
application and enriching the field with valuable end- and
professional user feedback.<br>
<br>
This Special Issue aims to collect papers on a broad spectrum of
specific topics reflecting recent advances in the methodology,
design and applicability of BCI. The following are indicative of
the kind of topics under discussion:<br>
<br>
</p>
<ul>
<li> Low-cost, portable, unobtrusive and robust sensors for
brain–computer interfaces;</li>
<li> Open-source software platforms for BCI;</li>
<li> The combination of brain imaging technologies with
physiological sensors</li>
<li> Brain–computer interface applications and user evaluation
studies;</li>
<li> Novel signal processing and machine learning for BCI, with
emphasis on transfer and deep learning methods;</li>
<li> New user training paradigms and advanced co-adaptive
approaches for BCI learning;</li>
<li> Benchmarking studies and production of big datasets BCI
methods.</li>
</ul>
<p><br>
</p>
<b>Guest Editors:</b><br>
<br>
Dr. Serafeim Perdikis<br>
Brain-Computer Interfaces and Neural Engineering Laboratory, School
of Computer Science and Electronic Engineering, University of Essex,
Colchester, UK<br>
<br>
Dr. Athanasios Vourvopoulos<br>
Institute for Systems and Robotics-Lisboa, Instituto Superior
Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal<br>
<p></p>
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