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Advanced EEG Sensing for Real-World Applications

A Special Issue of Sensors (ISSN 1424-8220) belonging to the section "Biomedical Sensors".

Deadline for manuscript submissions: 25 January 2027 | Viewed by 252

Editors


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Guest Editor
Department of Interdisciplinary Engineering, Daegu Gyeongbuk Institute of Science & Technology, Daegu 42988, Republic of Korea
Interests: human augmentation; human mobility interaction; automotive in-car UX; brain machine interface
Special Issues, Collections and Topics in MDPI journals
Department of Biomedical Software Engineering, The Catholic University of Korea, 43, Jibong-ro, Wonmi-gu, Bucheon-si, Gyeonggi-do, Republic of Korea
Interests: medical AI; brain-computer interface; EEG signal processing; neuroengineering; digital healthcare; explainable AI

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Guest Editor
Department of AI Convergence, College of Information Science, Hallym University, Chuncheon 24252, Republic of Korea
Interests: medical AI; LLM agent; brain-computer interface; EEG signal processing

Special Issue Information

Dear Colleagues,

Recent advances in electroencephalography (EEG) sensing technologies, wearable neural interfaces, and artificial intelligence (AI) have accelerated the development of intelligent brain-monitoring systems for real-world applications. Modern EEG platforms, combined with advanced signal processing and AI-based neural decoding algorithms, enable continuous, noninvasive, and context-aware interpretation of brain activity in naturalistic and clinically relevant environments.

This Special Issue, “Advanced EEG Sensing for Real-World Applications,” aims to highlight recent developments in EEG sensing technologies, computational intelligence, and translational neuroengineering. We welcome original research articles and comprehensive reviews addressing wearable and dry-electrode EEG systems, multimodal biosignal integration, artifact reduction, deep learning, explainable AI, foundation models, and real-time neural decoding methods.

Particular areas of interest include cognitive and mental state monitoring, physiological state estimation (e.g., motion sickness, drowsiness, fatigue, etc.), pain and affective state assessment, neurorehabilitation, human–machine interaction, digital therapeutics, personalized healthcare, and brain–computer interface (BCI) applications in daily life environments.

This Special Issue aligns with the scope of Sensors by focusing on advanced sensing technologies, intelligent biomedical signal analysis, and practical translational applications of EEG-based systems for healthcare and human-centered technologies.

Prof. Dr. Jinung An
Dr. Minji Lee
Dr. Dong-Ok Won
Guest Editors

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Keywords

  • EEG
  • wearable sensors
  • artificial intelligence
  • neural decoding
  • brain–computer interface
  • deep learning
  • explainable AI
  • foundation model
  • digital healthcare
  • neurotechnology
  • human–machine interaction
  • physiological state estimation

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Published Papers (1 paper)

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Research

39 pages, 6453 KB  
Article
Neurophysiological Characterization of ADHD in Children Using EEG Signals: A Machine Learning Approach to Executive Function Networks
by Diana Beatriz Gutiérrez-Jácome, Rosalynn Argelia Campos-Ortuño, José Eduardo Pardo-Valenzuela and Óscar Wladimir Gómez-Morales
Sensors 2026, 26(17), 5684; https://doi.org/10.3390/s26175684 - 7 Sep 2026
Abstract
Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder whose clinical assessment relies mainly on behavioral and neuropsychological evaluation. This study evaluates a subject-wise machine learning framework for distinguishing children with ADHD from healthy controls using multichannel EEG-derived features. The public dataset [...] Read more.
Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder whose clinical assessment relies mainly on behavioral and neuropsychological evaluation. This study evaluates a subject-wise machine learning framework for distinguishing children with ADHD from healthy controls using multichannel EEG-derived features. The public dataset comprised 121 participants (61 ADHD and 60 controls), with 19-channel EEG recordings sampled at 128 Hz. Signals were segmented into 4-s windows with 50% overlap, and statistical and spectral features were extracted, including mean, standard deviation, and theta-, alpha-, and beta-band power. Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting (GB), and Logistic Regression (LR) were evaluated using strict subject-wise separation. RF achieved the highest Accuracy (0.8099), F1-score (0.8160), Balanced Accuracy (0.8097), and MCC (0.6204), whereas SVM obtained the highest Sensitivity (0.8525) and ROC-AUC (0.8527). An additional subject-specific analysis based on individual alpha frequency (IAF) was performed to account for inter-individual spectral variability; mean IAF values were 8.8320 Hz for ADHD and 8.8833 Hz for controls, and the individualized-band analysis did not improve classification performance. Bootstrap confidence intervals and non-parametric tests indicated comparable performance among RF, SVM, and GB. Frontal and fronto-central channels, particularly Fz, showed the greatest model-derived contribution. Overall, the framework provides a reproducible subject-wise EEG classification approach, although external validation on independent cohorts remains necessary before clinical application. Full article
(This article belongs to the Special Issue Advanced EEG Sensing for Real-World Applications)
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