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Brain Sciences
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EEG and fMRI Applications in Exploring Brain Activity
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EEG and fMRI Applications in Exploring Brain Activity

A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Neurotechnology and Neuroimaging".

Deadline for manuscript submissions: 20 October 2026 | Viewed by 4421

Editors

Dr. Nicoletta Cera

E-Mail Website
Guest Editor
Faculty of Psychology and Education Sciences, University of Porto, 4200-135 Porto, Portugal
Interests: fMRI; brain methods; MRI; MRS; machine learning; statistics; neuroendocrinology
Special Issues, Collections and Topics in MDPI journals
Dr. Faustino Ricardo

E-Mail Website
Guest Editor Assistant
Research Unit in Medical Imaging and Radiotherapy, Cross I&D Lisbon Research Center, Escola Superior de Saúde da Cruz Vermelha Portuguesa, 1300-125 Lisbon, Portugal
Interests: neuroimaging; radiomics; MRI; biostatistics; machine learning

Special Issue Information

Dear Colleagues,

Background and history of this topic: In recent years, our understanding of the central nervous system has expanded significantly thanks to the use of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). These advancements have coincided with the development of more complex quantitative methods for analyzing MRI images and EEG signals. Additionally, researchers have created more sophisticated paradigms and designs to study emotional and cognitive functions.

Aim and scope of the Special Issue: The current Special Issue aims to gather significant advancements in EEG and fMRI applications, covering methods and theoretical aspects that help disentangle and explore brain activity. 

Cutting-edge research: Specifically, submissions should focus on novel advancements in EEG and fMRI in terms of methods and paradigms. 

What kind of papers we are soliciting: We strongly encourage the submission of EEG-, fMRI-, and multimodal-imaging-related studies. Meta-analyses and systematic reviews are also welcome.

Dr. Nicoletta Cera
Guest Editor

Dr. Faustino Ricardo
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 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-anonymized 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 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

  • fMRI
  • EEG
  • resting state
  • data-driven analysis
  • machine learning
  • emotion
  • cognitive functions
  • aging
  • multimodal imaging

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Published Papers (4 papers)

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Research

27 pages, 11681 KB  
Article
SleepStageNet: A Lightweight and Explainable Deep Learning Architecture for Multi-Channel Sleep Staging
by Ali Alhazmi
Brain Sci. 2026, 16(8), 860; https://doi.org/10.3390/brainsci16080860 - 14 Aug 2026
Viewed by 272
Abstract
Background/Objectives: Automatic sleep staging from polysomnography (PSG) is particularly challenging in clinical cohorts with neurological disorders. Methods: This study presents SleepStageNet, a compact model (1.075M parameters) that integrates a dual-branch convolutional epoch encoder, feature gating, a bidirectional gated recurrent unit, and multi-head self-attention [...] Read more.
Background/Objectives: Automatic sleep staging from polysomnography (PSG) is particularly challenging in clinical cohorts with neurological disorders. Methods: This study presents SleepStageNet, a compact model (1.075M parameters) that integrates a dual-branch convolutional epoch encoder, feature gating, a bidirectional gated recurrent unit, and multi-head self-attention for five-class staging from five PSG channels (C3, C4, EOG1, EOG2, and chin EMG). The individual operations are adapted from established architectures; the study contribution is their compact integration and controlled evaluation in an Indian acute stroke cohort. Results: Of the 100 recordings in the Indian Sleep Polysomnography (iSLEEPS) resource, 95 satisfied the five-channel extraction criteria, yielding 78,323 annotated epochs. Subject-independent 10-fold stratified group cross-validation produced an accuracy of 73.91 ± 2.24%, a macro F1-score of 67.29 ± 1.96%, and a Cohen’s κ of 0.634±0.029 (sample standard deviations). A matched single-branch encoder obtained κ=0.635 (full minus single branch: Δκ=−0.001, Holm-adjusted p=0.846), while matched C4-only input obtained κ=0.600 (full minus C4-only: Δκ=0.033, Holm-adjusted p=0.008). Grad-CAM and temporal attention visualizations provided qualitative evidence of physiologically plausible focus, while channel occlusion quantified the contribution of each signal. Without fine-tuning, a 10-model ensemble obtained κ=0.614 on ISRUC-SLEEP Subgroup III (10 healthy subjects; 8889 epochs). Conclusions: These results establish a reproducible reference for this clinical cohort while identifying the need for broader external and prospective validation. Full article
(This article belongs to the Special Issue EEG and fMRI Applications in Exploring Brain Activity)
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22 pages, 2022 KB  
Article
Disentangling EEG Fingerprinting and Sleep Biomarkers Using Generalized Weighted Ordinal Patterns
by Cristina Daiana Duarte, Albertina Arlenghi, Francisco Ramiro Iaconis, Gustavo Gasaneo and Claudio Delrieux
Brain Sci. 2026, 16(8), 793; https://doi.org/10.3390/brainsci16080793 - 28 Jul 2026
Viewed by 349
Abstract
Background: Electroencephalographic (EEG) recordings simultaneously contain information about neurophysiological dynamics and subject-specific characteristics. While this duality may enable biomarker discovery and individual identification, it also raises concerns that machine-learning models may achieve high predictive performance by exploiting subject identity rather than physiologically relevant [...] Read more.
Background: Electroencephalographic (EEG) recordings simultaneously contain information about neurophysiological dynamics and subject-specific characteristics. While this duality may enable biomarker discovery and individual identification, it also raises concerns that machine-learning models may achieve high predictive performance by exploiting subject identity rather than physiologically relevant information. Methods: In this study, we investigated whether generalized weighted ordinal patterns (GWOP), a statistical-complexity representation incorporating both temporal ordering and amplitude fluctuations, support sleep-stage classification while minimizing identity-related confounding. Sleep EEG recordings from 31 healthy subjects were segmented into 30-s epochs and represented using 3150 GWOP features derived from multiple embedding dimensions, time delays, and entropic indices. XGBoost classifiers were evaluated under intra-subject and inter-subject validation schemes to quantify the impact of EEG fingerprinting on sleep-stage classification performance. An additional subject-identification analysis was conducted using the same feature representation. Results: Sleep-stage classification generalized well to previously unseen subjects, with accuracy decreasing only from 79.2% to 75.8% between intra-subject and inter-subject evaluations. Feature-importance analysis using SHAP revealed an almost perfect correspondence between the features driving classification in both validation schemes (Spearman ρ=0.998). Conclusions: While this suggests that the models effectively generalize across subjects without being heavily confounded by individual identities, it indicates a framework of partial separation rather than complete orthogonality across the global feature space. In contrast, GWOP features also supported subject identification with 63.9% accuracy across the 31 individuals, demonstrating that GWOP preserve substantial fingerprinting information. The most informative features for subject identification showed little overlap with those governing sleep-stage classification, suggesting a partial separation between identity-related and biomarker-related information within the same feature space. These findings suggest that EEG fingerprinting and biomarker extraction are not necessarily competing objectives and support GWOP-based statistical-complexity measures as a promising proof-of-concept framework for robust sleep EEG analysis, serving as a foundation for future scale-up precision-neuroscience applications. Full article
(This article belongs to the Special Issue EEG and fMRI Applications in Exploring Brain Activity)
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23 pages, 5469 KB  
Article
Multi-Site Classification of Autism Spectrum Disorder Using Spatially Constrained ICA on Resting-State fMRI Networks
by Talha Imtiaz Baig, Junlin Jing, Peng Hu, Bochao Niu, Zhenzhen Yang, Bharat B. Biswal and Benjamin Klugah-Brown
Brain Sci. 2026, 16(2), 181; https://doi.org/10.3390/brainsci16020181 - 31 Jan 2026
Cited by 4 | Viewed by 1606
Abstract
Background/Objectives: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by differences in social communications and restricted, repetitive patterns of behaviors and interests, affecting approximately 1% of children globally. While functional magnetic resonance imaging (fMRI) has provided insights into altered brain [...] Read more.
Background/Objectives: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by differences in social communications and restricted, repetitive patterns of behaviors and interests, affecting approximately 1% of children globally. While functional magnetic resonance imaging (fMRI) has provided insights into altered brain connectivity patterns in ASD, classification based on neuroimaging remains a challenging due to the heterogeneity of the disorder and variability in imaging data across sites. This study employs a network-based approach using large-scale, multi-site rs-fMRI dataset from the Autism Brain Imaging Data Exchange (ABIDE I and II) to classify ASD and healthy controls using machine learning. Methods: A semi-blind Independent Component Analysis method, specifically the spatial constraint reference ICA, is applied to identify functional brain networks, and the ComBat harmonization technique is used to address site-specific variability across 11 independent datasets, ensuring consistency in feature representation. Support Vector Machines (SVMs) are employed for classification, focusing on three key networks: the Default Mode Network (DMN), Sensorimotor Network (SMN), and Visual Sensory Network (VSN). Results: The results demonstrate high classification accuracy, with the VSN achieving the highest performance (83.23% accuracy, 87.90% AUC), followed by the DMN (81.43% accuracy, 84.53% AUC) and the SMN (80.52% accuracy, 84.96% AUC), positioned with their recognized roles in social cognition and sensory–motor processing, respectively. Conclusions: The integration of ICA-based feature extraction with ComBat harmonization significantly improved classification accuracy compared to previous studies. These findings point out the potential of network-based approaches in ASD classification and point out the importance of integrating multi-site neuroimaging data for identifying reproduceable network-level features. Full article
(This article belongs to the Special Issue EEG and fMRI Applications in Exploring Brain Activity)
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20 pages, 2131 KB  
Article
Charting Early Brain Plasticity in Radiological Training: Functional Brain Reorganization During Early Radiological Expertise Acquisition
by Weilu Chai, Yuxin Bai, Jia Wu, Hongmei Wang, Jimin Liang, Xuemei Xie, Chenwang Jin and Minghao Dong
Brain Sci. 2025, 15(12), 1279; https://doi.org/10.3390/brainsci15121279 - 28 Nov 2025
Viewed by 990
Abstract
Background/Objectives: Radiological expertise draws on semantic knowledge and perceptual–cognitive mechanisms that support diagnostic reasoning. Early radiological training is a formative period when key cognitive processes begin to integrate. Nevertheless, how the brain pattern of early radiological expertise reorganizes during the first weeks of [...] Read more.
Background/Objectives: Radiological expertise draws on semantic knowledge and perceptual–cognitive mechanisms that support diagnostic reasoning. Early radiological training is a formative period when key cognitive processes begin to integrate. Nevertheless, how the brain pattern of early radiological expertise reorganizes during the first weeks of clinical exposure remains unknown, as prior work has relied mainly on cross-sectional designs comparing mature experts to beginners. Methods: We therefore conducted a longitudinal resting-state fMRI study in radiology interns (n = 43; 41 valid) scanned before and after short-term training. Behavioral performance improved significantly after training (p < 0.01). Regional homogeneity (ReHo) was computed for 246 Brainnetome ROIs for each subject. Results: Using a Support Vector Machine (SVM)-based recursive feature elimination (RFE) pipeline, 14 of these 246 features were identified as most discriminative, spanning regions involved in visual, semantic, memory, attentional, and decision-making processes. An SVM trained on these features effectively differentiated pre- and post-training brain states (training set: 86.67% accuracy, AUC = 0.97; validation set: 81.82% accuracy, AUC = 0.72). Conclusions: The observed neuroplastic changes provide direct evidence that multidimensional cognitive functions reorganize early in radiological expertise development and offer neural targets to inform evidence-based curriculum design, personalized training, and brain-targeted interventions (e.g., neuromodulation or neurofeedback) in radiology education. Full article
(This article belongs to the Special Issue EEG and fMRI Applications in Exploring Brain Activity)
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