Dear Colleagues,

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.

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:

• Low-cost, portable, unobtrusive and robust sensors for brain–computer interfaces;
• Open-source software platforms for BCI;
• The combination of brain imaging technologies with physiological sensors
• Brain–computer interface applications and user evaluation studies;
• Novel signal processing and machine learning for BCI, with emphasis on transfer and deep learning methods;
• New user training paradigms and advanced co-adaptive approaches for BCI learning;
• Benchmarking studies and production of big datasets BCI methods.

Dr. Serafeim Perdikis
Dr. Athanasios Vourvopoulos
Guest Editors

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