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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 December 2025 | Viewed by 11582

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Special Issue Editor

Prof. Dr. Alexander N. Pisarchik

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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
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Dear Colleagues,

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 hearing from you.

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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Published Papers (6 papers)

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Research

Jump to: Review

12 pages, 3072 KB

Open AccessArticle

Complex Network Responses to Regulation of a Brain-Computer Interface During Semi-Naturalistic Behavior

by Tengfei Feng, Halim Ibrahim Baqapuri, Jana Zweerings and Klaus Mathiak

Appl. Sci. 2025, 15(23), 12583; https://doi.org/10.3390/app152312583 - 27 Nov 2025

Viewed by 324

Abstract

Brain–computer interfaces (BCIs) can be used to monitor and provide real-time feedback on brain signals, directly influencing external systems, such as virtual environments (VE), to support self-regulation. We piloted a novel immersive, first-person shooting BCI-VE during which the avatars’ movement speed was directly influenced by neural activity in the supplementary motor area (SMA). Previous analyses revealed behavioral and localized neural effects for active versus reduced contingency neurofeedback in a randomized controlled trial design. However, the modeling of neural dynamics during such complex tasks challenges traditional event-related approaches. To overcome this limitation, we employed a data-driven framework utilizing group-level independent networks derived from BOLD-specific components of the multi-echo fMRI data obtained during the BCI regulation. Individual responses were estimated through dual regression. The spatial independent components corresponded to established cognitive networks and task-specific networks related to gaming actions. Compared to reduced contingency neurofeedback, active regulation induced significantly elevated fractional amplitude of low-frequency fluctuations (fALFF) in a frontoparietal control network, and spatial reweighting of a salience/ventral attention network, with stronger expression in SMA, prefrontal cortex, inferior parietal lobule, and occipital regions. These findings underscore the distributed network engagement of BCI regulation during a behavioral task in an immersive virtual environment. Full article

(This article belongs to the Special Issue Brain-Computer Interfaces: Development, Applications, and Challenges)

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21 pages, 1857 KB

Open AccessArticle

Effects of Prefrontal tDCS on Cognitive–Motor Performance During Postural Control and Isokinetic Strength Tasks in Women with Fibromyalgia: A Randomized, Sham-Controlled Crossover Study

by Mari Carmen Gomez-Alvaro, Maria Melo-Alonso, Narcis Gusi, Ricardo Cano-Plasencia, Juan Luis Leon-Llamas, Francisco Javier Domínguez-Muñoz and Santos Villafaina

Appl. Sci. 2025, 15(22), 12138; https://doi.org/10.3390/app152212138 - 15 Nov 2025

Viewed by 504

Abstract

This study investigated the effects of transcranial direct current stimulation (tDCS) over the dorsolateral prefrontal cortex (dlPFC) at three intensities (sham, 1 mA, 2 mA) on postural control, isokinetic strength, and cognitive performance in women with fibromyalgia (FM) and healthy controls (HCs). Using a double-blind, sham-controlled, crossover design, 26 participants (13 FM, 13 HC) completed sessions in randomized order, performing tasks under single- and dual-task conditions. Cognitive accuracy improved in both groups following 1 mA and 2 mA stimulation, particularly during single-task scenarios in static balance tasks. Notably, 2 mA tDCS reduced dual-task cost (DTC) in cognitive performance for the FM group, indicating decreased cognitive–motor interference. However, postural and strength outcomes showed no consistent intensity-dependent changes, with only selected nonlinear centers of pressure metrics (e.g., Lyapunov exponent, DFA) indicating possible modulation in FM. Isokinetic strength measures remained largely unaffected by tDCS across all intensities. Overall, the findings suggest that dlPFC-tDCS may selectively enhance cognitive function and reduce cognitive–motor interference in FM, especially under low-demand or higher-intensity stimulation conditions, while offering limited benefits for physical strength and balance. Full article

(This article belongs to the Special Issue Brain-Computer Interfaces: Development, Applications, and Challenges)

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19 pages, 3506 KB

Open AccessArticle

ERP Signatures of Stimulus Choice in Gaze-Independent BCI Communication

by Alice Mado Proverbio and Yldjana Dishi

Appl. Sci. 2025, 15(22), 11888; https://doi.org/10.3390/app152211888 - 8 Nov 2025

Viewed by 517

Abstract

This study aimed to identify electrophysiological markers (event-related potentials, ERPs) of intentional, need-related mental activity under controlled gaze fixation, with potential applications in brain–computer interface (BCI) development for individuals with severe motor impairments. Methods: Using stimuli from the PAIN Pictionary—a pictogram database for non-verbal communication in locked-in syndrome (LIS) contexts—neural responses were recorded via high-density EEG in 30 neurologically healthy adults (25 included after artifact-based exclusion). Participants viewed randomized sequences of pictograms representing ten fundamental need categories (e.g., “I am cold”, “I’m in pain”), with one category designated as the target per sequence. Each pictogram was followed by a visual cue prompting a button press: during training, participants executed the press; during the main task, they performed right-hand motor imagery while maintaining central fixation. Results: ERP analyses revealed a robust P300 response (450–650 ms; p < 0.0002) over centro-parietal regions for target cues, reflecting enhanced attentional allocation and stimulus choice. An early Contingent Negative Variation (CNV, 450–750 ms; p = 0.008) over fronto-lateral sites indicated anticipatory attention and motor preparation, while a left-lateralized late CNV (2250–2750 ms; p = 0.035) appeared to embody the preparation of a finalized motor plan for the forthcoming right-hand imagined response. A centro-parietal P600 component (600–800 ms; p = 0.044) emerged during response monitoring, reflecting evaluative and decisional processes. SwLORETA source analyses localized activity within a distributed network spanning prefrontal, premotor, motor, parietal, and limbic areas. Conclusions: These findings demonstrate that motor imagery alone can modulate pattern-onset ERP components without overt movement or gaze shifts, supporting the translational potential of decoding need-related intentions for thought-driven communication systems in individuals with profound motor impairments. Full article

(This article belongs to the Special Issue Brain-Computer Interfaces: Development, Applications, and Challenges)

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Review

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21 pages, 866 KB

Open AccessReview

Using VR and BCI to Improve Communication Between a Cyber-Physical System and an Operator in the Industrial Internet of Things

by Adrianna Piszcz, Izabela Rojek, Nataša Náprstková and Dariusz Mikołajewski

Appl. Sci. 2025, 15(23), 12805; https://doi.org/10.3390/app152312805 - 3 Dec 2025

Viewed by 391

Abstract

The Industry 5.0 paradigm places humans and the environment at the center. New communication methods based on virtual reality (VR) and brain–computer interfaces (BCIs) can improve system–operator interaction in multimedia communications, providing immersive environments where operators can more intuitively manage complex systems. The study was conducted through a systematic literature review combined with bibliometric and thematic analyses to map the current landscape of VR-BCI communication frameworks in IIoT environments. The methodology employed included structured resource selection, comparative assessment of interaction modalities, and cross-domain synthesis to identify patterns, gaps, and emerging technology trends. Key challenges identified include reliable signal processing, real-time integration of neural data with immersive interfaces, and the scalability of VR-BCI solutions in industrial applications. The study concludes by outlining future research directions focused on hybrid multimodal interfaces, adaptive cognition-based automation, and standardized protocols for evaluating human–cyber-physical system communication. VR interfaces enable operators to visualize and interact with network data in 3D, improving their monitoring and troubleshooting in real time. By integrating BCI technology, operators can control systems using neural signals, reducing the need for physical input devices and streamlining operation (including touchless technology). BCI-based protocols enable touchless control, which can be particularly useful in situations where operators must multitask, bypassing traditional input methods such as keyboards or mice. VR environments can simulate network conditions, allowing operators to practice and refine their responses to potential problems in a controlled, safe environment. Combining VR with BCI allows for the creation of adaptive interfaces that respond to the operator’s cognitive load, adjusting the complexity of the displayed information based on real-time neural feedback. This integration can lead to more personalized and effective training programs for operators, enhancing their skills and decision-making. VR and BCI-based solutions also have the potential to reduce operator fatigue by enabling more natural and intuitive interaction with complex systems. The use of these advanced technologies in multimedia telecommunications can translate into more efficient, precise, and user-friendly system management, ultimately improving service quality. Full article

(This article belongs to the Special Issue Brain-Computer Interfaces: Development, Applications, and Challenges)

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26 pages, 1351 KB

Open AccessReview

Trends and Limitations in Transformer-Based BCI Research

by Maximilian Achim Pfeffer, Johnny Kwok Wai Wong and Sai Ho Ling

Appl. Sci. 2025, 15(20), 11150; https://doi.org/10.3390/app152011150 - 17 Oct 2025

Viewed by 1470

Abstract

Transformer-based models have accelerated EEG motor imagery (MI) decoding by using self-attention to capture long-range temporal structures while complementing spatial inductive biases. This systematic survey of Scopus-indexed works from 2020 to 2025 indicates that reported advances are concentrated in offline, protocol-heterogeneous settings; inconsistent preprocessing, non-standard data splits, and sparse efficiency frequently reporting cloud claims of generalization and real-time suitability. Under session- and subject-aware evaluation on the BCIC IV 2a/2b dataset, typical performance clusters are in the high-80% range for binary MI and the mid-70% range for multi-class tasks with gains of roughly 5–10 percentage points achieved by strong hybrids (CNN/TCN–Transformer; hierarchical attention) rather than by extreme figures often driven by leakage-prone protocols. In parallel, transformer-driven denoising—particularly diffusion–transformer hybrids—yields strong signal-level metrics but remains weakly linked to task benefit; denoise → decode validation is rarely standardized despite being the most relevant proxy when artifact-free ground truth is unavailable. Three priorities emerge for translation: protocol discipline (fixed train/test partitions, transparent preprocessing, mandatory reporting of parameters, FLOPs, per-trial latency, and acquisition-to-feedback delay); task relevance (shared denoise → decode benchmarks for MI and related paradigms); and adaptivity at scale (self-supervised pretraining on heterogeneous EEG corpora and resource-aware co-optimization of preprocessing and hybrid transformer topologies). Evidence from subject-adjusting evolutionary pipelines that jointly tune preprocessing, attention depth, and CNN–Transformer fusion demonstrates reproducible inter-subject gains over established baselines under controlled protocols. Implementing these practices positions transformer-driven BCIs to move beyond inflated offline estimates toward reliable, real-time neurointerfaces with concrete clinical and assistive relevance. Full article

(This article belongs to the Special Issue Brain-Computer Interfaces: Development, Applications, and Challenges)

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46 pages, 1676 KB

Open AccessReview

Neural–Computer Interfaces: Theory, Practice, Perspectives

by Ignat Dubynin, Maxim Zemlyanskov, Irina Shalayeva, Oleg Gorskii, Vladimir Grinevich and Pavel Musienko

Appl. Sci. 2025, 15(16), 8900; https://doi.org/10.3390/app15168900 - 12 Aug 2025

Viewed by 7567

Abstract

This review outlines the technological principles of neural–computer interface (NCI) construction, classifying them according to: (1) the degree of intervention (invasive, semi-invasive, and non-invasive); (2) the direction of signal communication, including BCI (brain–computer interface) for converting neural activity into commands for external devices, CBI (computer–brain interface) for translating artificial signals into stimuli for the CNS, and BBI (brain–brain interface) for direct brain-to-brain interaction systems that account for agency; and (3) the mode of user interaction with technology (active, reactive, passive). For each NCI type, we detail the fundamental data processing principles, covering signal registration, digitization, preprocessing, classification, encoding, command execution, and stimulation, alongside engineering implementations ranging from EEG/MEG to intracortical implants and from transcranial magnetic stimulation (TMS) to intracortical microstimulation (ICMS). We also review mathematical modeling methods for NCIs, focusing on optimizing the extraction of informative features from neural signals—decoding for BCI and encoding for CBI—followed by a discussion of quasi-real-time operation and the use of DSP and neuromorphic chips. Quantitative metrics and rehabilitation measures for evaluating NCI system effectiveness are considered. Finally, we highlight promising future research directions, such as the development of electrochemical interfaces, biomimetic hierarchical systems, and energy-efficient technologies capable of expanding brain functionality. Full article

(This article belongs to the Special Issue Brain-Computer Interfaces: Development, Applications, and Challenges)

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