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Biomimetics
Special Issues
Advances in Brain–Computer Interfaces (BCI): Challenges and Opportunities
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Advances in Brain–Computer Interfaces (BCI): Challenges and Opportunities

A special issue of Biomimetics (ISSN 2313-7673). This special issue belongs to the section "Biological Optimisation and Management".

Deadline for manuscript submissions: 25 August 2025 | Viewed by 1200

Special Issue Editors

Dr. Shumao Xu

E-Mail Website
Guest Editor
1. Huck Institute of Life Science, The Pennsylvania State University, University Park, PA, USA
2. Institute of Science and Technology for Brain-Inspired Intelligence (ISTBI), Fudan University, Shanghai, China
Interests: neural probes; bioelectronics; brain-machine interface; neural interface; soft materials; additive manufacturing
Dr. Jing Wang

E-Mail Website
Guest Editor
Institute of Science and Technology for Brain-Inspired Intelligence (ISTBI), Fudan University, Shanghai, China
Interests: brain-computer integration; brain health; closed-loop prediction; digital therapeutic interventions; neural engineering; neurological rehabilitation

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to this Special Issue on "Advances in Brain–Computer Interfaces (BCI): Challenges and Opportunities". By enabling direct communication and interaction between neural processes and digital systems, BCIs open new avenues for enhancing human capabilities, and improving quality of life. However, they also raise significant issues related to biocompatibility, signal fidelity, and user interface optimization.

This Special Issue seeks to gather high-quality contributions that address critical topics in the development and application of BCI technologies. Contributions may focus on innovations in implantable microdevices that enhance connectivity, durability, and functionality within neuroprosthetic applications, as well as advancements in neural signal processing that involve cutting-edge algorithms and methodologies for interpreting and translating brain activity into actionable information. We encourage submissions that explore improvements in imaging techniques, offering better insights into neural dynamics and facilitating the real-time monitoring of BCI systems. Additionally, we invite research that delves into the implications of BCI technologies for cognitive enhancement and rehabilitation efforts for individuals with neurological impairments, alongside investigations into user-centered design approaches that prioritize intuitive and accessible interfaces to enhance user experience and engagement.

The multidisciplinary nature of this Special Issue is intended to foster collaboration among neuroscientists, engineers, clinicians, and technologists. By integrating diverse perspectives, we aspire to identify not only the challenges inherent in BCI development but also to explore innovative and sustainable solutions that maximize the benefits of this technology while ensuring alignment with safety standards and ethical integrity. We look forward to your valuable contributions, which will advance the scientific discourse in this dynamic and impactful field, paving the way for future innovations in brain-machine interfacing.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Development and optimization of implantable microdevices for BCI applications.
  • Advances in neural signal decoding algorithms and techniques.
  • Rehabilitation engineering approaches utilizing BCI technologies for enhancing cognitive functions.
  • User interface design innovations for improved accessibility and usability.
  • Cross-disciplinary studies integrating neuroscience, engineering, and technology.

We invite researchers from all backgrounds to share their insights and findings in this vital area of study. Your participation will contribute to advancing the field of BCIs and fostering a collaborative community dedicated to exploring the future possibilities and addressing the challenges of brain–machine connectivity.

We look forward to your valuable submissions.

Dr. Shumao Xu
Dr. Jing Wang
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. Biomimetics 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
  • implantable microdevices
  • imaging techniques
  • brain–machine connectivity
  • neural interfaces
  • neurotechnology
  • signal decoding and processing
  • cognitive enhancement
  • biomedical engineering
  • interface engineering

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

Published Papers (2 papers)

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Research

18 pages, 4988 KiB  
Article
A Capsule Decision Neural Network Based on Transfer Learning for EEG Signal Classification
by Wei Zhang, Xianlun Tang, Xiaoyuan Dang and Mengzhou Wang
Biomimetics 2025, 10(4), 225; https://doi.org/10.3390/biomimetics10040225 - 4 Apr 2025
Viewed by 254
Abstract
Transfer learning is the act of using the data or knowledge in a problem to help solve different but related problems. In a brain computer interface (BCI), it is important to deal with individual differences between topics and/or tasks. A kind of capsule [...] Read more.
Transfer learning is the act of using the data or knowledge in a problem to help solve different but related problems. In a brain computer interface (BCI), it is important to deal with individual differences between topics and/or tasks. A kind of capsule decision neural network (CDNN) based on transfer learning is proposed. In order to solve the problem of feature distortion caused by EEG feature extraction algorithm, a deep capsule decision network was constructed. The architecture includes multiple primary capsules to form a hidden layer, and the connection between the advanced capsule and the primary capsule is determined by the neural decision routing algorithm. Unlike the dynamic routing algorithm that iteratively calculates the similarity between primary capsules and advanced capsules, the neural decision network computes the relationship between each capsule in the deep and shallow hidden layers in a probabilistic manner. At the same time, the distribution of the EEG covariance matrix is aligned in Riemann space, and the regional adaptive method is further introduced to improve the independent decoding ability of the capsule decision neural network for the subject’s EEG signals. Experiments on two motor imagery EEG datasets show that CDNN outperforms several of the most advanced transfer learning methods. Full article
(This article belongs to the Special Issue Advances in Brain–Computer Interfaces (BCI): Challenges and Opportunities)
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15 pages, 1431 KiB  
Article
MSBiLSTM-Attention: EEG Emotion Recognition Model Based on Spatiotemporal Feature Fusion
by Yahong Ma, Zhentao Huang, Yuyao Yang, Zuowen Chen, Qi Dong, Shanwen Zhang and Yuan Li
Biomimetics 2025, 10(3), 178; https://doi.org/10.3390/biomimetics10030178 - 13 Mar 2025
Viewed by 652
Abstract
Emotional states play a crucial role in shaping decision-making and social interactions, with sentiment analysis becoming an essential technology in human–computer emotional engagement, garnering increasing interest in artificial intelligence research. In EEG-based emotion analysis, the main challenges are feature extraction and classifier design, [...] Read more.
Emotional states play a crucial role in shaping decision-making and social interactions, with sentiment analysis becoming an essential technology in human–computer emotional engagement, garnering increasing interest in artificial intelligence research. In EEG-based emotion analysis, the main challenges are feature extraction and classifier design, making the extraction of spatiotemporal information from EEG signals vital for effective emotion classification. Current methods largely depend on machine learning with manual feature extraction, while deep learning offers the advantage of automatic feature extraction and classification. Nonetheless, many deep learning approaches still necessitate manual preprocessing, which hampers accuracy and convenience. This paper introduces a novel deep learning technique that integrates multi-scale convolution and bidirectional long short-term memory networks with an attention mechanism for automatic EEG feature extraction and classification. By using raw EEG data, the method applies multi-scale convolutional neural networks and bidirectional long short-term memory networks to extract and merge features, selects key features via an attention mechanism, and classifies emotional EEG signals through a fully connected layer. The proposed model was evaluated on the SEED dataset for emotion classification. Experimental results demonstrate that this method effectively classifies EEG-based emotions, achieving classification accuracies of 99.44% for the three-class task and 99.85% for the four-class task in single validation, with average 10-fold-cross-validation accuracies of 99.49% and 99.70%, respectively. These findings suggest that the MSBiLSTM-Attention model is a powerful approach for emotion recognition. Full article
(This article belongs to the Special Issue Advances in Brain–Computer Interfaces (BCI): Challenges and Opportunities)
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