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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: closed (30 December 2025) | Viewed by 24065

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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

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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

Brain-Computer Interfaces: Development, Applications, and Challenges, 2nd Edition in Applied Sciences (3 articles)

Published Papers (11 papers)

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Editorial

Jump to: Research, Review

7 pages, 177 KB

Open AccessEditorial

Editorial for the 1st Edition Special Issue “Brain–Computer Interfaces: Development, Applications, and Challenges”

by Alexander N. Pisarchik

Appl. Sci. 2026, 16(6), 2701; https://doi.org/10.3390/app16062701 - 12 Mar 2026

Viewed by 539

Abstract

Brain–Computer Interface (BCI) technology stands as one of the most rapidly evolving and inherently multidisciplinary research frontiers in contemporary science and engineering [...] Full article

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

Research

Jump to: Editorial, Review

27 pages, 2073 KB

Open AccessArticle

SparseMambaNet: A Novel Architecture Integrating Bi-Mamba and a Mixture of Experts for Efficient EEG-Based Lie Detection

by Hanbeot Park, Yunjeong Cho and Hunhee Kim

Appl. Sci. 2026, 16(3), 1437; https://doi.org/10.3390/app16031437 - 30 Jan 2026

Viewed by 654

Abstract

Traditional lie detection technologies, such as the polygraph and event-related potential (ERP)-based approaches, often face limitations in real-world applicability due to their sensitivity to psychological states and the complex, nonlinear nature of electroencephalogram (EEG) signals. In this study, we propose SparseMambaNet, a novel neural architecture that integrates the recently developed Bi-Mamba model with a Sparsely Activated Mixture of Experts (MoE) structure to effectively model the intricate spatio-temporal dynamics of EEG data. By leveraging the near-linear computational complexity of Mamba and the bidirectional contextual modeling of Bi-Mamba, the proposed framework efficiently processes long EEG sequences while maximizing representational power through the selective activation of expert networks tailored to diverse input characteristics. Experiments were conducted with 46 healthy subjects using a simulated criminal scenario based on the Comparison Question Technique (CQT) with monetary incentives to induce realistic psychological tension. We extracted nine statistical and neural complexity features, including Hjorth parameters, Sample Entropy, and Spectral Entropy. The results demonstrated that Sample entropy and Hjorth parameters achieved exceptional classification performance, recording F1 scores of 0.9963 and 0.9935, respectively. Statistical analyses further revealed that the post-response “answer” interval provided significantly higher discriminative power compared to the “question” interval. Furthermore, channel-level analysis identified core neural loci for deception in the frontal and fronto-central regions, specifically at channels E54 and E63. These findings suggest that SparseMambaNet offers a highly efficient and precise solution for EEG-based lie detection, providing a robust foundation for the development of personalized brain–computer interface (BCI) systems in forensic and clinical settings. Full article

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

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19 pages, 3312 KB

Open AccessArticle

A Multi-Level EEG–EMG Neurofeedback Platform for Hand Rehabilitation After Stroke

by James Ailsworth, Rinku Roy, Jared Blaylock, David Reinkensmeyer and Derek Kamper

Appl. Sci. 2026, 16(3), 1336; https://doi.org/10.3390/app16031336 - 28 Jan 2026

Viewed by 989

Abstract

Hand rehabilitation in neurologic conditions such as stroke and cerebral palsy traditionally emphasizes repetitive task practice with visually observable feedback, despite motor impairment arising largely from abnormal neuromuscular activation. We present a platform that leverages noninvasive measurements of brain and muscle activity for neurofeedback-guided movement training. Trainees first learn to control EEG during movement preparation, followed by reciprocal control of finger muscle EMG during exoskeleton-assisted movement. We describe the platform design and two feasibility studies. Five neurotypical individuals learned to use EEG and EMG to drive an exoskeleton to grasp and release a virtual ball in a single session. They achieved a mean success rate of 65%, demonstrating improved movement latency (9%) and task completion time (6%) across the session. One individual post-stroke trained with the platform across eight sessions and exhibited improvements on the Box and Blocks Test, the Action Research Arm Test, and the Wolf Motor Function Test. These results demonstrate the feasibility of multi-level, neurofeedback training that targets neural activation throughout movement, rather than movement outcome alone. By explicitly engaging both cortical and muscular control signals, this paradigm offers a promising new direction for hand rehabilitation following neurologic injury. Full article

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

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17 pages, 1461 KB

Open AccessArticle

Semantic Latent Geometry Reveals Imagination–Perception Structure in EEG

by Hossein Ahmadi, Martina Impagnatiello and Luca Mesin

Appl. Sci. 2026, 16(2), 661; https://doi.org/10.3390/app16020661 - 8 Jan 2026

Viewed by 737

Abstract

We investigate whether representation-level, semantic diagnostics expose structure in electroencephalography (EEG) beyond conventional accuracy when contrasting perception and imagination and relating outcomes to self-reported imagery ability. Using a task-independent encoder that preserves scalp topology and temporal dependencies, we learn semantic features from multi-subject, multi-modal EEG (pictorial, orthographic, auditory) and evaluate subject-independent decoding with lightweight heads, achieving state-of-the-art or better accuracy with low variance across subjects. To probe the latent space directly, we introduce threshold-resolved correlation pruning and derive the Semantic Sensitivity Index (SSI) and cross-modal overlap (CMO). While correlations between Vividness of Visual Imagery Questionnaire (VVIQ)/Bucknell Auditory Imagery Scale (BAIS) and leave-one-subject-out (LOSO) accuracy are small and imprecise at n = 12, the semantic diagnostics reveal interpretable geometry: for several subjects, imagination retains a more compact, non-redundant latent subset than perception (positive SSI), and a substantial cross-modal core emerges (CMO ≈ 0.5–0.8). These effects suggest that accuracy alone under-reports cognitive organization in the learned space and that semantic compactness and redundancy patterns capture person-specific phase preferences. Given the small cohort and the subjectivity of questionnaires, the findings argue for semantic, representation-aware evaluation as a necessary complement to accuracy in EEG-based decoding and trait linkage. Full article

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

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18 pages, 6272 KB

Open AccessArticle

Using Virtual Reality to Promote Cognitive Engagement in Rett Syndrome: Eye-Tracking Evidence from Immersive Forest Tasks

by Rosa Angela Fabio, Michela Perina, Andrea Nucita, Giancarlo Iannizzotto and Martina Semino

Appl. Sci. 2026, 16(2), 626; https://doi.org/10.3390/app16020626 - 7 Jan 2026

Viewed by 805

Abstract

Rett syndrome (RTT) is a rare neurodevelopmental disorder that causes severe motor and cognitive impairments, limiting voluntary communication. Gaze-based technologies and virtual reality (VR) offer innovative ways to assess and enhance attention, happiness, and learning in individuals with minimal motor control. This study investigated and compared visual-attentional and emotional engagement in girls with RTT and typically developing (TD) peers during exploration of a virtual forest presented in 2D and immersive 3D (VR) formats across four progressively complex tasks. Twelve girls with RTT and 12 TD peers completed eye-tracking tasks measuring reaction time, fixation duration, disengagement events, and observed happiness. Girls with RTT showed slower responses and more disengagements overall, but VR significantly improved attentional efficiency in both groups, resulting in faster reaction times (η²_p = 0.36), longer fixations (η²_p = 0.31), and fewer disengagements (η²_p = 0.27). These effects were stronger in the RTT group. Both groups also showed greater happiness in VR settings (RTT: p = 0.011; TD: p = 0.015), and in participants with RTT, peaks in attention coincided with peak happiness, indicating a link between happiness and cognitive engagement. Immersive VR thus appears to enhance attention and affect in RTT, supporting its integration into personalized neurorehabilitation. Full article

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

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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 874

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 1283

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 1000

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

Jump to: Editorial, Research

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

Cited by 3 | Viewed by 1357

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

Cited by 2 | Viewed by 3012

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

Cited by 1 | Viewed by 11287

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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