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Brain Sciences
Special Issues
Non-Invasive Neurotechnologies for Cognitive Augmentation
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Non-Invasive Neurotechnologies for Cognitive Augmentation

A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Neural Engineering, Neuroergonomics and Neurorobotics".

Deadline for manuscript submissions: 15 August 2026 | Viewed by 2029

Special Issue Editors

Dr. Caterina Cinel

E-Mail Website
Guest Editor
Brain-Computer Interfaces and Neural Engineering Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK
Interests: cognitive neuroscience; EEG; brain–computer interfaces; neuroergonomics; cognitive augmentation; human–computer interaction
Special Issues, Collections and Topics in MDPI journals
Dr. Davide Valeriani

E-Mail Website
Guest Editor
Technogym UK, Bracknell, UK
Interests: brain–computer interfaces; wearable devices; health technology
Dr. Saugat Bhattacharyya

E-Mail Website
Guest Editor
School of Computing, Engineering and Intelligent Systems, Ulster University, Northern Ireland, Londonderry BT48 7JL, UK
Interests: cognitive neuroscience; artificial intelligence; machine learning and its application in human–machine interaction and neurorehabilitation; brain–computer interfaces
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Interest in cognitive augmentation has surged over the past two decades, driven by advances in brain–computer interfaces (BCIs), neuroimaging, and brain stimulation. This Special Issue invites cutting-edge contributions exploring the use of non-invasive neurotechnologies to enhance human cognition. We welcome original research and reviews on both closed-loop and open-loop BCIs designed for cognitive augmentation, including attention, memory, learning, and decision-making. Topics of interest include human–machine interaction mediated by non-invasive neurotechnology; neuroergonomics; brain stimulation techniques such as tDCS and TMS; neural decoding of cognitive states; adaptive neurotechnologies leveraging AI; clinical applications; and neurorehabilitation. We also encourage interdisciplinary work addressing the ethical, legal, and social implications of neuroenhancement. This issue aims to showcase advances that bridge neuroscience, engineering, and ethics, highlighting both current applications and future directions in non-invasive cognitive technologies. Researchers from academia, industry, and clinical practice are encouraged to contribute.

Dr. Caterina Cinel
Dr. Davide Valeriani
Dr. Saugat Bhattacharyya
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 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. Brain Sciences 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

  • neurotechnology
  • brain–computer interfaces
  • neuroergonomics
  • neurorehabilitation
  • neuroethics
  • brain stimulation
  • cognitive augmentation
  • cognitive enhancement
  • adaptive neurotechnology

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Published Papers (2 papers)

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Research

23 pages, 2831 KB  
Article
Interpretable Network-Level Biomarker Discovery for Alzheimer’s Stage Assessment Using Resting-State fNIRS Complexity Graphs
by Min-Kyoung Kang, Agatha Elisabet, So-Hyeon Yoo and Keum-Shik Hong
Brain Sci. 2026, 16(2), 239; https://doi.org/10.3390/brainsci16020239 - 19 Feb 2026
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Abstract
Background/Objectives: This study introduces a reproducible and interpretable graph-based framework for resting-state functional near-infrared spectroscopy (fNIRS) that enables network-level biomarker discovery for Alzheimer’s disease (AD). Although resting-state fNIRS is well suited for task-free assessment, most existing approaches rely on static channel-wise features [...] Read more.
Background/Objectives: This study introduces a reproducible and interpretable graph-based framework for resting-state functional near-infrared spectroscopy (fNIRS) that enables network-level biomarker discovery for Alzheimer’s disease (AD). Although resting-state fNIRS is well suited for task-free assessment, most existing approaches rely on static channel-wise features or conventional functional connectivity, limiting insight into coordinated network dynamics and reproducibility. Methods: Resting-state prefrontal fNIRS signals were represented as subject-level graphs in which edges captured coordinated fluctuations of nonlinear signal complexity across channels, computed using sliding-window analysis. Graph neural networks (GNNs) were employed as analytical tools to identify disease-stage-related network patterns. Interpretability was assessed using edge-level importance measures, and reproducibility was evaluated through fold-wise stability analysis and consensus network construction. Results: The proposed complexity–fluctuation-based graph representation consistently outperformed conventional amplitude-based functional connectivity. Statistically supported prefrontal network biomarkers distinguishing mild cognitive impairment (MCI) from healthy aging were identified, with statistically significant group differences (p = 0.001). In contrast, network patterns associated with Alzheimer’s disease were more heterogeneous and less consistently expressed. Consensus analysis revealed a subset of prefrontal connections repeatedly selected across cross-validation folds, and attention-based network patterns showed strong spatial correspondence with statistically derived biomarkers. Conclusions: This study establishes a reproducible and interpretable framework for resting-state fNIRS analysis that emphasizes coordinated complexity dynamics rather than classification accuracy. The results indicate that network-level alterations are most consistently expressed at the MCI stage, highlighting its role as a critical transitional state and supporting the potential of the proposed approach for longitudinal monitoring and clinically applicable fNIRS-based assessment of neurodegenerative disease. Full article
(This article belongs to the Special Issue Non-Invasive Neurotechnologies for Cognitive Augmentation)
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14 pages, 1583 KB  
Article
Facilitating Novice Visual Search with tES over rIFG: Baseline-Dependent Gains in Target Identification
by Bradley M. Robert, Aaron Winder, Mason S. Briggs, Gabriella I. Atencio and Vincent P. Clark
Brain Sci. 2026, 16(1), 1; https://doi.org/10.3390/brainsci16010001 - 19 Dec 2025
Viewed by 488
Abstract
Background/Objectives: Transcranial electrical stimulation (tES) shows potential for enhancing attention and learning, yet its effects in applied contexts remain underexplored. This study investigated whether transcranial direct current stimulation (tDCS) either alone or in combination with high-frequency transcranial random noise stimulation (hf-tRNS) over the [...] Read more.
Background/Objectives: Transcranial electrical stimulation (tES) shows potential for enhancing attention and learning, yet its effects in applied contexts remain underexplored. This study investigated whether transcranial direct current stimulation (tDCS) either alone or in combination with high-frequency transcranial random noise stimulation (hf-tRNS) over the right inferior frontal gyrus (rIFG) could enhance performance in a visual search task requiring target identification and change detection, compared with a low-current control condition. Methods: Sixty-four participants were randomly assigned to receive tDCS alone (2.0 mA), tDCS with hf-tRNS (1.8 mA DC offset combined with 100–500 Hz noise at ±0.18 mA), or low-current control stimulation during training. The task involved identifying vehicles and detecting changes between image presentations. Performance accuracy and EEG oscillatory power were assessed at baseline and post-stimulation. Results: ANCOVA revealed significant effects of stimulation on target identification accuracy (F(2,60) = 3.27, p = 0.045, ηp2 = 0.098), with tDCS showing greater improvement than the low-current control condition (p = 0.017). No significant effects were found for change detection for any stimulation condition, or for either the target or change detection for hf-tRNS. Baseline performance moderated stimulation effects: low performers receiving tDCS showed the greatest improvements (F(2,26) = 3.80, p = 0.036, ηp2 = 0.226), surpassing even high-baseline performers post-training. EEG analyses revealed that participants who showed greater decreases in frontal theta power demonstrated larger improvements in accuracy with tDCS alone (r = −0.634, p = 0.005) but not with hf-tRNS or the control. Conclusions: tDCS over rIFG selectively enhanced target identification accuracy in a complex visual search, particularly benefiting individuals with lower-baseline performance. These findings suggest tDCS may facilitate training in lower-performing novice populations. Full article
(This article belongs to the Special Issue Non-Invasive Neurotechnologies for Cognitive Augmentation)
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