Advanced Applications of Brain–Computer Interfaces in Neuroscience

A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Neural Engineering, Neuroergonomics and Neurorobotics".

Deadline for manuscript submissions: 11 November 2024 | Viewed by 1082

Special Issue Editors

Department of Psychology, Sapienza University of Rome, 00185 Roma, Italy
Interests: neuropsychology; mental imagery; executive functions; navigation; eye move-ments; EMDR

E-Mail Website
Guest Editor
Computer Science and Telecommunication, eCampus University, 22060 Novedrate, Italy
Interests: artificial intelligence; IoT; database; computer vision; human computer interfaces
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Electrical, Electronic and Computer Engineering, University of Ca-tania, 95125 Catania, Italy
Interests: biomedical informatics; EEG; biometrics; signal theory; RMI
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, brain–computer interfaces (BCIs) have emerged as pioneering applications, becoming an ever growing field of the interplay between neuroscience, neuropsychology and advanced computer technology. This Special Issue aims to offer a compendium of innovative research and state-of-the-art advancements, unraveling the multifaceted tapestry of applications that BCIs offer in deciphering, understanding and leveraging the language of the human brain. Starting from a variety of approaches, this Special Issue will collect novel researches and applications from a wide spectrum of perspectives. From decoding neural signals for enhanced prosthetic control to leveraging BCIs as diagnostic tools in neurological conditions, this Special Issue will explore the many ways in which BCIs are reshaping the landscape of neuroscience.

A small (non-limiting) list of topics we are interested on covering are as follows:

  • Cognitive Enhancement through BCIs: exploring the potential of BCIs in augmenting cognitive functions, such as memory enhancement and attention improvement.
  • BCIs in Neurorehabilitation: examining the applications of BCIs in rehabilitation protocols for stroke survivors and individuals with neurological disorders, focusing on motor and cognitive recovery.
  • Real-Time Neural Monitoring: discussing advancements in the real-time monitoring of neural activity using BCIs for understanding brain dynamics during various cognitive tasks.
  • BCIs and Neurofeedback: investigating the use of BCIs for neurofeedback applications, particularly in the context of mental health and stress management.
  • BCIs for Communication and Language Processing: analyzing the role of BCIs in facilitating communication for individuals with communication disorders and exploring language processing interfaces.
  • BCIs and Virtual Reality: exploring the integration of BCIs with virtual reality technologies for immersive neurorehabilitation and cognitive training experiences.
  • Neural Interface Design and User Experience: examining the design principles and user experience considerations in developing BCIs to ensure seamless integration into daily life.
  • BCIs in Brain–Machine Collaborations: investigating collaborative efforts between the human brain and machines, focusing on applications in robotics, automation and other technological interfaces.
  • BCIs and Neuroplasticity: assessing the impact of BCIs on neuroplasticity, exploring how neural interfaces can induce adaptive changes in the brain over time.
  • BCIs in Neuropsychiatric Disorders: studying the potential therapeutic applications of BCIs in neuropsychiatric disorders, including applications in mood disorders and anxiety management.
  • Neural Data Privacy and Security: discussing the challenges and strategies for ensuring the privacy and security of neural data collected through BCIs.
  • BCIs and Cognitive Neuroscience Research: highlighting the contributions of BCIs to fundamental cognitive neuroscience research, including studies on perception, attention and decision-making.
  • BCIs and Brain–Machine Learning Interfaces: exploring the intersection of BCIs and machine learning algorithms for developing adaptive and personalized neural interfaces.

Dr. Samuele Russo
Dr. Cristian Randieri
Dr. Francesco Beritelli
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Brain Sciences is an international peer-reviewed open access monthly 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 2200 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.

Keywords

  • cognitive enhancement
  • neurorehabilitation
  • real-time neural monitoring
  • virtual reality integration
  • neural feedback
  • neuroplasticity
  • machine learning interfaces
  • brain–machine collaboration

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

20 pages, 1707 KiB  
Article
Real-Time fMRI Neurofeedback Training of Selective Attention in Older Adults
by Tian Lin, Mohit Rana, Peiwei Liu, Rebecca Polk, Amber Heemskerk, Steven M. Weisberg, Dawn Bowers, Ranganatha Sitaram and Natalie C. Ebner
Brain Sci. 2024, 14(9), 931; https://doi.org/10.3390/brainsci14090931 - 18 Sep 2024
Viewed by 720
Abstract
Background: Selective attention declines with age, due to age-related functional changes in dorsal anterior cingulate cortex (dACC). Real-time functional magnetic resonance imaging (rtfMRI) neurofeedback has been used in young adults to train volitional control of brain activity, including in dACC. Methods: For the [...] Read more.
Background: Selective attention declines with age, due to age-related functional changes in dorsal anterior cingulate cortex (dACC). Real-time functional magnetic resonance imaging (rtfMRI) neurofeedback has been used in young adults to train volitional control of brain activity, including in dACC. Methods: For the first time, this study used rtfMRI neurofeedback to train 19 young and 27 older adults in volitional up- or down-regulation of bilateral dACC during a selective attention task. Results: Older participants in the up-regulation condition (experimental group) showed greater reward points and dACC BOLD signal across training sessions, reflective of neurofeedback training success; and faster reaction time and better response accuracy, suggesting behavioral benefits on selective attention. These effects were not observed for older participants in the down-regulation condition (inverse condition control group), supporting specificity of volitional dACC up-regulation training in older adults. These effects were, unexpectedly, also not observed for young participants in the up-regulation condition (age control group), perhaps due to a lack of motivation to continue the training. Conclusions: These findings provide promising first evidence of functional plasticity in dACC in late life via rtfMRI neurofeedback up-regulation training, enhancing selective attention, and demonstrate proof of concept of rtfMRI neurofeedback training in cognitive aging. Full article
(This article belongs to the Special Issue Advanced Applications of Brain–Computer Interfaces in Neuroscience)
Show Figures

Figure 1

Back to TopTop