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Applied Sciences
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Brain-Computer Interfaces: Development, Applications, and Challenges, 2nd Edition
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Brain-Computer Interfaces: Development, Applications, and Challenges, 2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Neuroscience and Neural Engineering".

Deadline for manuscript submissions: 30 July 2026 | Viewed by 1590

Special Issue Editor

Prof. Dr. Alexander N. Pisarchik

E-Mail Website
Guest Editor
Center for Biomedical Technology, Technical University of Madrid, Campus Montegancedo, Pozuelo de Alarcón, 28223 Madrid, Spain
Interests: complex systems; bioinformatics; mathematical and computational biology; optics and photonics; biological physics; cognitive neuroscience
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is a continuation of our previous Special Issue, "Brain-Computer Interfaces: Development, Applications, and Challenges".

Brain–Computer Interface (BCI) technology is a rapidly evolving multidisciplinary research area with a wide range of applications in medicine, neurorehabilitation, robotics, gaming, assistive technologies, and human–machine interaction. This Special Issue aims to bring together recent developments in BCI systems and explore their integration into practical, real-world solutions. We invite high-quality original research articles, reviews, and case studies addressing the design, development, and application of BCIs. Particular attention will be given to innovative methods for signal acquisition, processing, classification, and the interpretation of brain activity, as well as their use in real-time control systems.

Submissions are especially encouraged in the following application domains:

  • Brain control of robotic limbs, avatars, exoskeletons, and assistive devices;
  • Detection, prediction, and prevention of neurological and psychiatric disorders;
  • Assessment and modulation of psychophysiological states (e.g., fatigue, stress, and attention);
  • Monitoring of cognitive functions in both healthy and clinical populations.

This Special Issue will serve as a platform to highlight the current challenges and future directions in BCI research and its transformative potential across disciplines.

We look forward to receiving your contributions.

Prof. Dr. Alexander N. Pisarchik
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 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. Applied Sciences 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 2400 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)
  • neurotechnology
  • EEG signal processing
  • cognitive and affective state monitoring
  • neural control of robotics
  • biomedical applications of BCIs
  • real-time brain signal analysis
  • human–machine interaction

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Related Special Issue

Published Papers (3 papers)

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Research

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24 pages, 6987 KB  
Article
Assessing the Impact of an Assistive Driving Device on Mental Workload and Stress During Simulated Driving: A Multivariate Approach
by Simone Costantini, Camilla Zanco, Alfonso Mastropietro, Sara Arlati, Giuseppe Andreoni, Giovanna Rizzo, Fabio Alexander Storm and Marta Mondellini
Appl. Sci. 2026, 16(10), 4974; https://doi.org/10.3390/app16104974 - 16 May 2026
Viewed by 135
Abstract
Driving with assistive devices creates complex cognitive and emotional demands that require systematic investigation. This study uses a multivariate approach based on subjective and objective measures to evaluate mental workload (MWL), stress and emotional state during simulated driving with an assistive device. Thirty [...] Read more.
Driving with assistive devices creates complex cognitive and emotional demands that require systematic investigation. This study uses a multivariate approach based on subjective and objective measures to evaluate mental workload (MWL), stress and emotional state during simulated driving with an assistive device. Thirty healthy adults (42±13 years of age, 7 females) completed four driving tasks combining two levels of difficulty (Easy vs. Hard) and two steering tools (wheel vs. single-pin aid). Subjective measures from NASA Task Load Index and Self-Assessment Manikin were collected, as well as physiological parameters from electroencephalographic, electrocardiographic, and electrodermal activity signals. The results revealed that the assistive device significantly induced increases in perceived physical demand, frustration, loss of emotional control and stress, yet reducing intrinsic sympathetic response represented by electrodermal activity parameters. Multivariate analyses highlighted that combining different physiological predictors improved MWL estimation. This study marks an initial step towards understanding the impact of assistive devices on MWL and stress in post-acute individuals returning to driving. Full article
(This article belongs to the Special Issue Brain-Computer Interfaces: Development, Applications, and Challenges, 2nd Edition)
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20 pages, 1069 KB  
Article
Low-Latency Test-Time Adaptation for Inter-Subject SSVEP Decoding via Online Euclidean Alignment and Frequency-Regularized Entropy Minimization
by Sheng-Bin Duan and Jianlong Hao
Appl. Sci. 2026, 16(8), 3799; https://doi.org/10.3390/app16083799 - 13 Apr 2026
Viewed by 344
Abstract
Electroencephalography (EEG)-based brain–computer interface (BCI) systems are often affected by substantial inter-subject variability. These differences cause distribution shifts between the source domain and the target domain. As a result, the decoder’s generalization to unseen subjects is reduced. In online steady-state visual evoked potentials [...] Read more.
Electroencephalography (EEG)-based brain–computer interface (BCI) systems are often affected by substantial inter-subject variability. These differences cause distribution shifts between the source domain and the target domain. As a result, the decoder’s generalization to unseen subjects is reduced. In online steady-state visual evoked potentials (SSVEP)-based BCI systems, the decoder must not only cope with inter-subject distribution shifts but also adapt rapidly. However, most existing methods require accumulating multiple trials before adaptation, which increases data acquisition and update latency and thus limits their practicality in online settings. To address these challenges, this study focuses on a practically important but insufficiently explored setting, which is unlabeled inter-subject SSVEP decoding with single-trial online adaptation, where immediate adaptation is required and multi-trial accumulation is impractical. For this setting, this study proposes a low-latency test-time adaptation algorithm that combines trial-wise online Euclidean alignment, entropy minimization, and pseudo-label frequency regularization. This integration supports single-trial adaptation under online constraints, without requiring target labels or trial buffering, thereby reducing adaptation latency while mitigating inter-subject distribution shift. Experiments on two public datasets using four backbone models show that the proposed method achieves an average accuracy of 75.70%, outperforming the non-adaptive baseline by 3.88%. These results indicate that the proposed method improves inter-subject SSVEP decoding accuracy and shows potential for online BCI applications. Full article
(This article belongs to the Special Issue Brain-Computer Interfaces: Development, Applications, and Challenges, 2nd Edition)
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Review

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27 pages, 1201 KB  
Review
Brain–Computer Interfaces in Learning Disorders and Mathematical Learning: A Scoping Review with Structured Narrative Synthesis
by Viktoriya Galitskaya, Georgios Polydoros, Alexandros-Stamatios Antoniou, Pantelis Pergantis and Athanasios Drigas
Appl. Sci. 2026, 16(8), 3846; https://doi.org/10.3390/app16083846 - 15 Apr 2026
Viewed by 655
Abstract
Brain–Computer Interfaces (BCIs) have increasingly been explored as tools for monitoring and modulating cognitive processes relevant to learning. However, their application to learning disorders, and especially to mathematical learning difficulties such as dyscalculia and ageometria, remains conceptually promising but empirically underdeveloped. The present [...] Read more.
Brain–Computer Interfaces (BCIs) have increasingly been explored as tools for monitoring and modulating cognitive processes relevant to learning. However, their application to learning disorders, and especially to mathematical learning difficulties such as dyscalculia and ageometria, remains conceptually promising but empirically underdeveloped. The present study offers a scoping review with structured narrative synthesis of recent empirical research on BCI-based interventions in learning disorder populations, with particular attention paid to their possible translational relevance for mathematical learning. Following PRISMA-ScR principles and a Population–Concept–Context framework, studies published between 2020 and 2025 were identified through database searches in Scopus, IEEE Xplore, and PubMed. A total of 30 studies met the inclusion criteria. All eligible studies focused on Attention-Deficit/Hyperactivity Disorder (ADHD), while no eligible BCI intervention studies were found for dyscalculia or ageometria. The reviewed literature was dominated by EEG-based neurofeedback interventions. To move beyond descriptive summary, the included studies were organized using a structured analytical framework based on intervention modality, primary cognitive target, methodological robustness, and translational proximity to mathematical learning disorders. Across the evidence base, the most consistent findings concerned attention regulation and executive function outcomes, whereas academic and mathematics-related outcomes were sparse and methodologically less developed. Although several studies suggested improvements in domain-general cognitive mechanisms relevant to mathematical learning, the absence of direct evidence in dyscalculia and ageometria prevents confirmatory conclusions. The review therefore identifies both the promise and the limits of current BCI applications in learning disorder contexts and argues that future research should prioritize theory-driven, disorder-specific trials targeting numeracy, visuospatial reasoning, and executive processes in mathematical learning disabilities. Although current findings suggest promising cognitive and educational potential, these technologies are not yet ready for routine implementation in standard classroom environments without further validation, teacher training, ethical safeguards, and cost-effective deployment models. Full article
(This article belongs to the Special Issue Brain-Computer Interfaces: Development, Applications, and Challenges, 2nd Edition)
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