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Brain Sciences
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The Use of the Brain–Computer Interface (BCI) in Neuroscience
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The Use of the Brain–Computer Interface (BCI) in Neuroscience

A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Computational Neuroscience and Neuroinformatics".

Deadline for manuscript submissions: closed (15 April 2025) | Viewed by 1209

Special Issue Editor

Prof. Dr. Yasue Mitsukura

E-Mail Website
Guest Editor
Department of Technology and Engineering, Keio University, Yokohama 2238532, Japan
Interests: brain-computer interface; artificial intelligence; neuroscience

Special Issue Information

Dear Colleagues,

In a rapidly evolving technological landscape, the convergence of Brain-Computer Interface (BCI) technology and multimedia systems has unlocked unprecedented opportunities for human–machine interactions. This groundbreaking Special Issue delves into the fascinating realm where neuroscience meets artificial intelligence, promising a future where communication and interaction are reshaped.

Key Highlights:

  • The Neurological Frontier: Dive into the fundamentals of Brain-Computer Interfaces, exploring the science behind mind-to-machine communication. Learn how BCIs have transcended from science fiction to reality, enabling direct communication with computers through neural signals.
  • Empowering Individuals with Disabilities: Discover the transformative impact of BCIs in empowering individuals with physical disabilities. Witness real-world applications where BCIs enable paralyzed individuals to regain control over their environments, communicate and even interact with multimedia content.
  • Chat-Type AI and Multimodal Interactions: Uncover the transformative potential of chat-type AI in enhancing multimedia systems. Explore how AI-powered chatbots, such as the one you are conversing with now, can seamlessly integrate into multimedia platforms, offering personalized, context-aware experiences.
  • The Synergy of BCI and Multimedia: Explore case studies showcasing the synergy between BCIs and multimedia systems. Witness how individuals can control multimedia content, such as videos, music and virtual environments, using their thoughts, and how AI can enhance these interactions.
  • Ethical Considerations: Engage in a thought-provoking discussion on the ethical implications of BCI and AI integration. Address questions surrounding privacy, consent, data security and the potential misuse of brain data.
  • The Road Ahead: Gain insights into the future of BCI and multimedia systems, including advancements on the horizon and potential breakthroughs. Discuss the challenges and opportunities that lie ahead in this rapidly advancing field.

This Special Issue brings together experts at the intersection of neuroscience, artificial intelligence and multimedia technology to provide attendees with a comprehensive understanding of the evolving landscape. Whether you are a researcher, developer or simply curious about the future of human–machine interactions, join us as we embark on a journey into the exciting world of Brain-Computer Interfaces and multimedia systems enhanced by chat-type AI. Explore the possibilities, ask questions and envision a future where the power of thought converges with the realm of digital content and communication.

Prof. Dr. Yasue Mitsukura
Guest Editor

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

  • brain-computer interface
  • human–machine interactions
  • artificial intelligence
  • neuroscience

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Published Papers (1 paper)

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Research

17 pages, 7193 KiB  
Article
Effects of Packet Loss on Neural Decoding Effectiveness in Wireless Transmission
by Jiaqi Zheng, Yuan Li, Liangliang Chen, Fei Wang, Boxuan Gu, Qixiang Sun, Xiang Gao and Fan Zhou
Brain Sci. 2025, 15(3), 221; https://doi.org/10.3390/brainsci15030221 - 20 Feb 2025
Viewed by 813
Abstract
Background: In brain–computer interfaces, neural decoding plays a central role in translating neural signals into meaningful physical actions. These signals are transmitted to processors for decoding via wired or wireless channels; however, they are often subject to data loss, commonly referred to as [...] Read more.
Background: In brain–computer interfaces, neural decoding plays a central role in translating neural signals into meaningful physical actions. These signals are transmitted to processors for decoding via wired or wireless channels; however, they are often subject to data loss, commonly referred to as “packet loss”. Despite their importance, the effects of different types and degrees of packet loss on neural decoding have not yet been comprehensively studied. Understanding these effects is critical for advancing neural signal processing. Methods: This study addresses this gap by constructing four distinct packet loss models that simulate the congestion, distribution, and burst loss scenarios. Using macaque superior arm movement decoding experiments, we analyzed the effects of the aforementioned packet loss types on decoding performance across six parameters (position, velocity, and acceleration in the x and y dimensions). The performance was assessed using the R2 metric and statistical comparisons across different loss scenarios. Results: Our results indicate that sudden, consecutive packet loss significantly degraded decoding performance. For the same packet loss probability, burst loss led to the largest decrease in the R2 value. Notably, when the packet loss rate reached 10%, the decoding performance for acceleration dropped to 73% of the original R2 value. On the other hand, when the packet loss rate was within 2%, the neural signal decoding results across all packet loss models remained largely unaffected. However, as the packet loss rate increased, the impact became more pronounced. These findings highlight the varying degrees to which different packet loss models affect decoding outcomes. Conclusions: This study quantitatively evaluated the relationship between packet loss and neural decoding outcomes, highlighting the differential effects of loss patterns on decoding parameters, and it proposed some methods and devices to solve the problem of packet loss. These findings offer valuable insights for the development of resilient neural signal acquisition and processing systems capable of mitigating the impact of packet loss. Full article
(This article belongs to the Special Issue The Use of the Brain–Computer Interface (BCI) in Neuroscience)
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