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EEG-Based Sensing Applications for Health Monitoring and Interventions in Chronic Disorders

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: 20 January 2026 | Viewed by 1078

Special Issue Editor


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Guest Editor
1. International research center for Cognitive Applied Neuroscience (IrcCAN), Università Cattolica del Sacro Cuore, Largo Gemelli 1, 20123 Milan, Italy
2. Research Unit in Affective and Social Neuroscience, Department of Psychology, Università Cattolica del Sacro Cuore, Largo Gemelli 1, 20123 Milan, Italy
Interests: frequency band analysis; social neuroscience; chronic conditions; addiction; EEG sensing

Special Issue Information

Dear Colleagues,

The past decade has seen significant advances in health services, driven by electronic health records and the advent of e-health technology. A new wave of technology-based behavioral health interventions that involve the delivery of evidence-informed practices via computers, web-based applications, mobile phones, wearable sensors, or other technological platforms is reshaping behavioral healthcare. These interventions also include applications such as EEG-based sensing devices, which collect neural biomarkers to distinguish features of neurocognitive impairment; this is especially relevant in chronic disorders such as addiction. Such approaches align with the concept of precision medicine, offering personalized care and tracking disease progression.

In addition, EEG-based neurofeedback interventions, which provide real-time feedback on brain activity and enable individuals to regulate their brain function, could be especially useful in managing addiction, where neurofeedback can assist in altering the neural pathways associated with addictive behaviors. These interventions enhance traditional therapies, offering a non-invasive, personalized approach that could significantly improve treatment outcomes for chronic disorders, particularly addiction. 

This Special Issue aims to provide an overview of recent developments regarding the use of EEG-based sensing devices for enhancing the assessment and treatment practices applied to chronic neuropsychiatric disorders, with a specific focus on substance use disorders and behavioral addictions. Additionally, it intends to collect studies showcasing the current state of research in technology-based behavioral health interventions at the intersection of neuroscience, addiction medicine, and neuroengineering.

Authors are encouraged to submit cutting-edge case studies, original research papers and reviews on various aspects of the use of neuro-technology and technology in the diagnosis and treatment of addiction (both substance use disorders and behavioral addictions) and chronic disorders.

Dr. Laura Angioletti
Guest Editor

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Keywords

  • e-health
  • mental health
  • health monitoring
  • EEG based sensors
  • EEG neurofeedback
  • wearable devices
  • chronic disorders
  • substance use disorders
  • behavioral addiction

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Published Papers (1 paper)

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Research

26 pages, 7555 KB  
Article
EEG Spectral Analysis in Chronic Pain During Rest and Cognitive Reasoning
by Diana Chertic, Victor Dăbală, Livia Livinț-Popa, Maria Balea, Nicu Cătălin Drăghici, Ștefan Strilciuc, Răzvan Cherecheș, Vitalie Văcăraș and Dafin F. Mureșanu
Sensors 2025, 25(19), 6230; https://doi.org/10.3390/s25196230 - 8 Oct 2025
Viewed by 718
Abstract
Chronic pain (CP) represents a multidimensional condition in which cognitive and emotional factors shape the individual experience from perception to action. The purpose of this study was to characterize the functional significance of alterations in neural oscillatory dynamics underlying the transition from resting-state [...] Read more.
Chronic pain (CP) represents a multidimensional condition in which cognitive and emotional factors shape the individual experience from perception to action. The purpose of this study was to characterize the functional significance of alterations in neural oscillatory dynamics underlying the transition from resting-state to cognitive load across distinct CP phenotypes. Continuous electroencephalographic data were acquired from patients with headache, migraine, and spine-related pain, as well as healthy controls, during rest and three visual–cognitive–motor (VCM) tasks: reaction time, working memory, and associative learning. First, within CP subgroups, we examined cognitive-load-related changes in oscillatory activity. In migraine patients, alpha/beta power attenuation induced during cognitive processing correlated with higher reported pain intensity. Relative to the spine-related pain group, migraine patients exhibited increased occipital alpha and gamma band activity during working memory and associative learning conditions, as a possible neurophysiological signature of cortical hyperexcitability. By comparing a subset of headache patients to healthy controls, we found elevated resting-state delta and gamma activity in the patient group. Under cognitive load conditions, headache patients showed higher power across delta, theta, beta, and gamma frequency bands. Delta and theta activity elicited during the working memory task correlated negatively with pain intensity. Our results demonstrate that the experience of chronic pain is accompanied by frequency-specific alterations in both resting and cognitive-associated oscillatory dynamics, reflecting impaired visual working-memory processing and top–down modulation of behaviorally relevant stimuli. Full article
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