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EEG Horizons: Exploring Neural Dynamics and Neurocognitive Processes
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EEG Horizons: Exploring Neural Dynamics and Neurocognitive Processes

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: 20 January 2026 | Viewed by 2461

Special Issue Editors

Prof. Dr. Giacinto Barresi

E-Mail Website
Guest Editor
Bristol Robotics Laboratory, University of the West of England, Bristol BS16 1QY, UK
Interests: neuroergonomics; biomedical robotics; human–robot interaction; human augmentation; rehabilitation technology; assistive technology; prosthetics; extended reality; digital health; gamification
Special Issues, Collections and Topics in MDPI journals
Dr. Yelena Tonoyan

E-Mail Website
Guest Editor Assistant
Rehab Technologies Lab, Istituto Italiano di Tecnologia, Via Morego, 30-16163 Genoa, Italy
Interests: computational neuroscience; human–robot interaction; cognitive neuroscience
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Electroencephalography (EEG) is one of the key tools for understanding how our brain enables us to navigate complex systems (from monitoring the difficulty of focusing on delicate tasks like surgeries to controlling an assistive robot through a brain-computer interface, BCI), and it also offers insights on an individual’s state that help create novel solutions for healthcare (investigating and detecting biomarkers of conditions like diverse forms of epilepsy).

This Special Issue focuses on the latest insights offered by EEG in exploring neural dynamics and neurocognitive processes in various contexts, including clinical applications. We aim to collect contributions that leverage EEG as a tool for detecting, evaluating, and interpreting brain activity, fostering a discussion on the advancements in areas like computational neuroscience, neurotechnology, neuroengineering, neuroergonomics, and the human-centered design of neuroadaptive systems. We welcome contributions investigating EEG-based techniques, methodologies, indices, and biomarkers for offering an overview of state of the art (from connectomics to neuromodulation) within this fertile and versatile approach to studying and assisting the human brain.

Prof. Dr. Giacinto Barresi
Guest Editor

Dr. Yelena Tonoyan
Guest Editor Assistant

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. 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

  • electroencephalography (EEG)
  • brain–computer interface (BCI)
  • brain activity
  • brain diseases
  • neural dynamics
  • neurocognitive
  • computational neuroscience
  • neurotechnology
  • neuro-engineering
  • neuroergonomics
  • neuroadaptive systems
  • connectomics
  • neuromodulation

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Published Papers (3 papers)

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Research

13 pages, 1554 KB  
Article
Modulation of a Rubber Hand Illusion by Different Levels of Mental Workload: An EEG Study
by Yelena Tonoyan, Stefano Maludrottu, Nicolò Boccardo, Luca Berdondini, Matteo Laffranchi and Giacinto Barresi
Appl. Sci. 2025, 15(17), 9682; https://doi.org/10.3390/app15179682 - 3 Sep 2025
Viewed by 491
Abstract
The current study aimed to investigate the impact of externally evoked mental workload on the level of an artificial hand ownership sensation, a component of the embodiment phenomenon (feeling an external object, in this case a fake upper limb, as part of one’s [...] Read more.
The current study aimed to investigate the impact of externally evoked mental workload on the level of an artificial hand ownership sensation, a component of the embodiment phenomenon (feeling an external object, in this case a fake upper limb, as part of one’s body). The process of embodiment is extensively investigated in the literature also to find solutions for promoting the acceptance of prosthetic limbs. Before a traditional procedure for summoning in healthy subjects a Rubber Hand Illusion (RHI), the participants performed memory-related tasks in easy or demanding conditions to generate, respectively, low and high mental workloads. Alongside the behavioral correlates of the body ownership in the form of a proprioceptive drift (the measure of the correspondence between the perceived position of the actual limb and the fake one), EEG data was also collected. The results, both behavioral and neural, suggest that a high mental workload before the RHI experience leads to a low level of body ownership, whereas a low one enhances it. This can be interpreted as a consequence of distracting mental resources (possibly a specific type of them) from the embodiment stimulation session. Full article
(This article belongs to the Special Issue EEG Horizons: Exploring Neural Dynamics and Neurocognitive Processes)
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18 pages, 1588 KB  
Article
EEG-Based Attention Classification for Enhanced Learning Experience
by Madiha Khalid Syed, Hong Wang, Awais Ahmad Siddiqi, Shahnawaz Qureshi and Mohamed Amin Gouda
Appl. Sci. 2025, 15(15), 8668; https://doi.org/10.3390/app15158668 - 5 Aug 2025
Viewed by 868
Abstract
This paper presents a novel EEG-based learning system designed to enhance the efficiency and effectiveness of studying by dynamically adjusting the difficulty level of learning materials based on real-time attention levels. In the training phase, EEG signals corresponding to high and low concentration [...] Read more.
This paper presents a novel EEG-based learning system designed to enhance the efficiency and effectiveness of studying by dynamically adjusting the difficulty level of learning materials based on real-time attention levels. In the training phase, EEG signals corresponding to high and low concentration levels are recorded while participants engage in quizzes to learn and memorize Chinese characters. The attention levels are determined based on performance metrics derived from the quiz results. Following extensive preprocessing, the EEG data undergoes several feature extraction steps: removal of artifacts due to eye blinks and facial movements, segregation of waves based on their frequencies, similarity indexing with respect to delay, binary thresholding, and (PCA). These extracted features are then fed into a k-NN classifier, which accurately distinguishes between high and low attention brain wave patterns, with the labels derived from the quiz performance indicating high or low attention. During the implementation phase, the system continuously monitors the user’s EEG signals while studying. When low attention levels are detected, the system increases the repetition frequency and reduces the difficulty of the flashcards to refocus the user’s attention. Conversely, when high concentration levels are identified, the system escalates the difficulty level of the flashcards to maximize the learning challenge. This adaptive approach ensures a more effective learning experience by maintaining optimal cognitive engagement, resulting in improved learning rates, reduced stress, and increased overall learning efficiency. Our results indicate that this EEG-based adaptive learning system holds significant potential for personalized education, fostering better retention and understanding of Chinese characters. Full article
(This article belongs to the Special Issue EEG Horizons: Exploring Neural Dynamics and Neurocognitive Processes)
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34 pages, 3704 KB  
Article
Uncertainty-Aware Deep Learning for Robust and Interpretable MI EEG Using Channel Dropout and LayerCAM Integration
by Óscar Wladimir Gómez-Morales, Sofia Escalante-Escobar, Diego Fabian Collazos-Huertas, Andrés Marino Álvarez-Meza and German Castellanos-Dominguez
Appl. Sci. 2025, 15(14), 8036; https://doi.org/10.3390/app15148036 - 18 Jul 2025
Viewed by 661
Abstract
Motor Imagery (MI) classification plays a crucial role in enhancing the performance of brain–computer interface (BCI) systems, thereby enabling advanced neurorehabilitation and the development of intuitive brain-controlled technologies. However, MI classification using electroencephalography (EEG) is hindered by spatiotemporal variability and the limited interpretability [...] Read more.
Motor Imagery (MI) classification plays a crucial role in enhancing the performance of brain–computer interface (BCI) systems, thereby enabling advanced neurorehabilitation and the development of intuitive brain-controlled technologies. However, MI classification using electroencephalography (EEG) is hindered by spatiotemporal variability and the limited interpretability of deep learning (DL) models. To mitigate these challenges, dropout techniques are employed as regularization strategies. Nevertheless, the removal of critical EEG channels, particularly those from the sensorimotor cortex, can result in substantial spatial information loss, especially under limited training data conditions. This issue, compounded by high EEG variability in subjects with poor performance, hinders generalization and reduces the interpretability and clinical trust in MI-based BCI systems. This study proposes a novel framework integrating channel dropout—a variant of Monte Carlo dropout (MCD)—with class activation maps (CAMs) to enhance robustness and interpretability in MI classification. This integration represents a significant step forward by offering, for the first time, a dedicated solution to concurrently mitigate spatiotemporal uncertainty and provide fine-grained neurophysiologically relevant interpretability in motor imagery classification, particularly demonstrating refined spatial attention in challenging low-performing subjects. We evaluate three DL architectures (ShallowConvNet, EEGNet, TCNet Fusion) on a 52-subject MI-EEG dataset, applying channel dropout to simulate structural variability and LayerCAM to visualize spatiotemporal patterns. Results demonstrate that among the three evaluated deep learning models for MI-EEG classification, TCNet Fusion achieved the highest peak accuracy of 74.4% using 32 EEG channels. At the same time, ShallowConvNet recorded the lowest peak at 72.7%, indicating TCNet Fusion’s robustness in moderate-density montages. Incorporating MCD notably improved model consistency and classification accuracy, especially in low-performing subjects where baseline accuracies were below 70%; EEGNet and TCNet Fusion showed accuracy improvements of up to 10% compared to their non-MCD versions. Furthermore, LayerCAM visualizations enhanced with MCD transformed diffuse spatial activation patterns into more focused and interpretable topographies, aligning more closely with known motor-related brain regions and thereby boosting both interpretability and classification reliability across varying subject performance levels. Our approach offers a unified solution for uncertainty-aware, and interpretable MI classification. Full article
(This article belongs to the Special Issue EEG Horizons: Exploring Neural Dynamics and Neurocognitive Processes)
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