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Applied Sciences
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EEG-Based Wearable Devices for Body Monitoring
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EEG-Based Wearable Devices for Body Monitoring

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: 20 July 2026 | Viewed by 1615

Special Issue Editors

Dr. Rui Xu

E-Mail Website
Guest Editor
Medical School, Tianjin University, Tianjin 300072, China
Interests: neural rehabilitation mechanisms; motor rehabilitation assessment and training technologies/equipment
Dr. Shuang Qiu

E-Mail Website
Guest Editor
Laboratory of Brain Atlas and Brain-Inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
Interests: brain-computer interface; neural modulation; information encoding and decoding

Special Issue Information

Dear Colleagues,

This Special Issue focuses on advancing research and innovation in the development and application of EEG-based wearable devices for comprehensive body monitoring. Recent advancements in neurotechnology, sensor miniaturization, and signal processing have enabled the design of portable, non-invasive systems capable of capturing high-quality brain activity data in real-world settings. These devices could revolutionize such areas as neurological disorder diagnosis, cognitive state assessment, sleep monitoring, rehabilitation, and brain–computer interfaces (BCIs). 

This Special Issue seeks contributions that explore novel hardware designs, signal enhancement algorithms, machine learning techniques for EEG data interpretation, and integration with multimodal sensing platforms (e.g., ECG, EMG, or motion sensors). We also welcome studies that address challenges in usability, long-term wearability, energy efficiency, and ethical considerations. Submissions may include original research, reviews and case studies for healthcare, sports science, and personalized wellness. 

Dr. Rui Xu
Dr. Shuang Qiu
Guest Editors

Manuscript Submission Information

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Keywords

  • wearable devices
  • body monitoring
  • brain-computer interface (BCI)
  • neurotechnology
  • signal processing
  • biomedical sensors
  • health monitoring
  • neurological disorders
  • real-time data analysis

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Published Papers (2 papers)

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28 pages, 9929 KB  
Article
Cross-Subject EEG Mental State Recognition via Correlation-Based Feature Selection
by Edson Masao Odake, Jr., Diego Resende Faria and Eduardo Parente Ribeiro
Appl. Sci. 2026, 16(2), 1011; https://doi.org/10.3390/app16021011 - 19 Jan 2026
Viewed by 325
Abstract
Electroencephalography (EEG) provides valuable information about a subject’s mental state; however, developing reliable classification models remains challenging. One major difficulty lies in defining an effective feature representation, as the wide range of features proposed in the literature often leads to high-dimensional inputs, increasing [...] Read more.
Electroencephalography (EEG) provides valuable information about a subject’s mental state; however, developing reliable classification models remains challenging. One major difficulty lies in defining an effective feature representation, as the wide range of features proposed in the literature often leads to high-dimensional inputs, increasing the risk of overfitting, reducing generalization, and raising computational cost. A further critical challenge is the strong inter-subject variability inherent to EEG data, where distributional shifts frequently cause models trained on one individual to perform poorly on unseen subjects. This work proposes a novel family of correlation-based feature selection methods that explicitly models inter-feature relationships through correlation structures. The objective is to identify features that are simultaneously discriminative across mental states (relaxed and concentrated) and invariant across subjects, thereby improving cross-subject generalization. The proposed methods are evaluated against established feature selection and dimensionality reduction techniques using a leave-one-subject-out experimental protocol, in which models are trained on multiple participants and tested on unseen individuals. Experimental results demonstrate that the proposed approach consistently achieves superior or competitive performance compared to existing methods, particularly under strong inter-subject distribution shifts. In addition, the analysis reveals how preprocessing parameters—such as window length, overlap, and frequency band decomposition—affect classification performance and generalization. Unlike previous EEG feature selection approaches that primarily focus on feature relevance or redundancy, the proposed framework explicitly promotes domain invariance while preserving feature interpretability, without relying on subject-specific calibration. Full article
(This article belongs to the Special Issue EEG-Based Wearable Devices for Body Monitoring)
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25 pages, 721 KB  
Systematic Review
EEG-Based Assessment of Mental Fatigue in Students: A Systematic Review of Measurement Methods and Data Processing Protocols
by Rosa Ayuso-Moreno, Ana Rubio-Morales, Alba Durán-Rufaco, Tomás García-Calvo and Inmaculada González-Ponce
Appl. Sci. 2026, 16(1), 234; https://doi.org/10.3390/app16010234 - 25 Dec 2025
Viewed by 770
Abstract
Mental fatigue significantly impairs student performance and learning outcomes, yet reliable neurophysiological assessment methods remain elusive in educational research. This systematic review examines electroencephalography (EEG) as an objective monitoring tool for mental fatigue in student populations, with particular focus on portable and wearable [...] Read more.
Mental fatigue significantly impairs student performance and learning outcomes, yet reliable neurophysiological assessment methods remain elusive in educational research. This systematic review examines electroencephalography (EEG) as an objective monitoring tool for mental fatigue in student populations, with particular focus on portable and wearable device applications. Following PRISMA guidelines, we systematically analysed 18 empirical studies (2012–2024, N = 595 participants, ages 10–32) employing continuous EEG during educational tasks. We evaluated frequency band definitions, EEG hardware configurations (from 4-channel portable devices to 64-channel research systems), electrode placements, preprocessing pipelines, and analytical approaches, including machine learning methods. Most studies identified increased frontal theta (4–8 Hz) and decreased beta (13–30 Hz) power as primary fatigue markers across diverse EEG systems. However, substantial methodological heterogeneity emerged: frequency band definitions varied considerably, preprocessing techniques differed, and small sample sizes (median N = 20) limited statistical power. While portable EEG systems demonstrate promise for objective, non-invasive cognitive state monitoring in naturalistic educational settings, current methodological inconsistencies constrain reliability and validity. This review identifies critical standardisation gaps and provides evidence-based recommendations for wearable EEG device development and implementation, including standardised protocols, automated artifact removal strategies, and validation linking EEG measures to educational outcomes. Full article
(This article belongs to the Special Issue EEG-Based Wearable Devices for Body Monitoring)
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