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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 417

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 (1 paper)

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Research

34 pages, 3703 KiB  
Article
Uncertainty-AwareDeep 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 99
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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