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Bioengineering
Special Issues
Brain Computer Interfaces for Motor Control and Motor Learning
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Brain Computer Interfaces for Motor Control and Motor Learning

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: closed (30 May 2025) | Viewed by 497

Special Issue Editor

Dr. Ramana Kumar Vinjamuri

E-Mail Website
Guest Editor
Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA
Interests: brain–computer interfaces; neuroprosthetics and exoskeletons; machine learning; signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Brain–computer interfaces (BCIs) represent a fascinating intersection of neuroscience and engineering. By directly decoding brain signals, BCIs offer the potential to revolutionize how we interact with technology and the world around us. There are several applications of BCIs in neuroprosthetics, communication, gaming and entertainment, neurorehabilitation, and augmented cognition. While there are interesting applications, there are also several challenges, including signal noise and variability, real-time processing, and last but not least, ethical considerations. The future of BCIs lies in improving BCI performance in the long term, using AI and ML methodologies in BCIs, expanding BCI applications, and developing wireless invasive and noninvasive BCIs that are reliable and safe to use.

Dr. Ramana Kumar Vinjamuri
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. Bioengineering 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 2700 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 (BCI)
  • motor control
  • motor learning
  • AI and ML in BCIs
  • neuroprosthetics
  • neurorehabilitation

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Published Papers (1 paper)

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Research

24 pages, 890 KiB  
Article
MCTGNet: A Multi-Scale Convolution and Hybrid Attention Network for Robust Motor Imagery EEG Decoding
by Huangtao Zhan, Xinhui Li, Xun Song, Zhao Lv and Ping Li
Bioengineering 2025, 12(7), 775; https://doi.org/10.3390/bioengineering12070775 - 17 Jul 2025
Viewed by 261
Abstract
Motor imagery (MI) EEG decoding is a key application in brain–computer interface (BCI) research. In cross-session scenarios, the generalization and robustness of decoding models are particularly challenging due to the complex nonlinear dynamics of MI-EEG signals in both temporal and frequency domains, as [...] Read more.
Motor imagery (MI) EEG decoding is a key application in brain–computer interface (BCI) research. In cross-session scenarios, the generalization and robustness of decoding models are particularly challenging due to the complex nonlinear dynamics of MI-EEG signals in both temporal and frequency domains, as well as distributional shifts across different recording sessions. While multi-scale feature extraction is a promising approach for generalized and robust MI decoding, conventional classifiers (e.g., multilayer perceptrons) struggle to perform accurate classification when confronted with high-order, nonstationary feature distributions, which have become a major bottleneck for improving decoding performance. To address this issue, we propose an end-to-end decoding framework, MCTGNet, whose core idea is to formulate the classification process as a high-order function approximation task that jointly models both task labels and feature structures. By introducing a group rational Kolmogorov–Arnold Network (GR-KAN), the system enhances generalization and robustness under cross-session conditions. Experiments on the BCI Competition IV 2a and 2b datasets demonstrate that MCTGNet achieves average classification accuracies of 88.93% and 91.42%, respectively, outperforming state-of-the-art methods by 3.32% and 1.83%. Full article
(This article belongs to the Special Issue Brain Computer Interfaces for Motor Control and Motor Learning)
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