Brain Computer Interface for Biomedical Applications

Dear Colleagues,

Over the years, brain–computer Interfaces (BCIs) have shown remarkable potential in revolutionizing biomedical applications, enabling communication, interaction, and motor function restoration for individuals with disabilities. Recently, passive BCIs have emerged, capable of detecting cognitive aspects, such as mental load, attention, and stress.

The successful translation of brain signals into meaningful messages relies on methodological factors, like preprocessing, channel selection methods, feature extraction, and classification algorithms, which remain active areas of research and development. Notably, the use of brain connectivity measures in feature extraction has garnered growing interest for enhancing overall BCI performance. AI methods, including transfer learning approaches, play a pivot role in achieving broad accessibility. They enable the development of personalized rehabilitation plans and decoding complex patterns in biomedical applications.

On the other hand, in clinical and rehabilitative applications, a user-centric approach is vital, emphasizing high flexibility and portability in BCI systems to adapt easily to individual users. Additionally, investigating feedback, rewards, and reinforcement strategies considering emotional and motivational aspects is encouraged, as well as investigations on neural mechanisms underlying transfer-of-benefit in behavioral and/or motor performance induced by plasticity-based BCI systems.

This Special Issue is dedicated at exploring the latest innovations, studies, and developments in the context of BCIs for biomedical applications. It aims to present the state-of-the-art advancements, current challenges, and future trends related to the successful application of BCIs.

Potential topics include, but are not limited to, the following:

• Advanced BCI technologies for motor and cognitive rehabilitation;
• BCI applications for communication and assistive technologies;
• Active and passive BCIs;
• Development, advantages, and challenges of non-invasive and invasive BCIs;
• Signal processing of EEG, fMRI, NIRS data for BCIs;
• Exploring functional brain connectivity and graph analysis in BCIs;
• Machine learning and deep learning in BCIs;
• Investigation of transfer learning and domain adaptation in BCIs;
• Neuro-feedback and BCI;
• Exploring the link between beneficial BCI-systems effects and neural substrates;
• Investigating different types and role of feedback/reward in BCI.

Dr. Silvia Francesca Storti
Dr. Stefano Silvoni
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.