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Sensing Technologies and Machine Learning for Cognitive and Physiological Monitoring
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Sensing Technologies and Machine Learning for Cognitive and Physiological Monitoring

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

Deadline for manuscript submissions: 10 January 2027 | Viewed by 1455

Special Issue Editor

Dr. Carlos Valderrama

E-Mail Website
Guest Editor
Electronics and Microelectronics, Polytechnic Faculty, University of Mons, 7000 Mons, Belgium
Interests: electronics; microelectronics; EDA; ESL; FPGA
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue of Sensors will focus on the integration of cutting-edge sensor technologies and machine learning (ML) for advancing cognitive and physiological monitoring. It will bring together innovative research and applications that leverage embedded systems, wearable biosensors, and state-of-the-art signal processing techniques to enable real-time, accurate, and personalized health monitoring.

This issue will highlight the role of AI-enhanced sensor fusion in detecting cognitive states and explore emerging sensor technologies for brain–computer interface (BCI) systems, including real-time applications. It will also address the development of energy-efficient sensor architectures and low-power ML models for continuous, wearable physiological monitoring. Additionally, the issue will cover personalized machine learning approaches tailored to individual cognitive and physiological profiles, as well as the use of generative ML and Edge AI for elevated monitoring capabilities.

By showcasing advancements in multi-modal sensor technologies and the fusion of sensor data with ML for real-time cognitive assessment, this Special Issue will provide a comprehensive overview of the latest developments in sensor-driven, AI-powered solutions for healthcare and human–machine interaction. It will serve as a valuable resource for researchers and practitioners working at the intersection of sensor technology, artificial intelligence, and health monitoring.

Topics:

  1. Embedded systems for physiological monitoring applications.
  2. AI-enhanced sensor fusion for cognitive state detection.
  3. Advanced signal processing for wearable biosensors.
  4. Advanced sensor technologies for brain–computer interface (BCI) systems.
  5. Emerging sensors for real-time brain-computer interface applications
  6. The fusion of sensor data and machine learning for real-time cognitive state assessment.
  7. Energy-efficient sensor architectures for continuous health monitoring.
  8. Low-power machine learning architectures for wearable physiological sensors.
  9. Personalized machine learning models for physiological and cognitive monitoring.
  10. Generative machine learning for cognitive and physiological monitoring.
  11. Edge AI for cognitive and physiological monitoring.
  12. Advanced sensor technologies for multi-modal physiological monitoring.

Dr. Carlos Valderrama
Guest Editor

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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors 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 2600 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

  • AI-enhanced sensor fusion
  • wearable biosensors for cognitive monitoring
  • Edge AI for real-time health monitoring
  • brain–computer interface (BCI) sensors
  • personalized machine learning in healthcare
  • energy-efficient sensor architectures

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Published Papers (1 paper)

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Research

26 pages, 1346 KB  
Article
Machine Learning-Based Comparative Analysis of Subject-Independent EEG Data Classification Across Multiple Meditation and Non-Meditation Sessions
by Nalinda D. Liyanagedera, Corinne A. Bareham, Heather Kempton and Hans W. Guesgen
Sensors 2025, 25(22), 6876; https://doi.org/10.3390/s25226876 - 11 Nov 2025
Viewed by 1084
Abstract
In this study, subject-independent (inter-subject), multiple-session electroencephalography (EEG) data classification was tested for loving-kindness meditation (LKM) and non-meditation. This is a novel study that extends our previous work on intra-subject, multiple-session classification. Here, two meditation techniques, LKM-Self and LKM-Other, were independently compared with [...] Read more.
In this study, subject-independent (inter-subject), multiple-session electroencephalography (EEG) data classification was tested for loving-kindness meditation (LKM) and non-meditation. This is a novel study that extends our previous work on intra-subject, multiple-session classification. Here, two meditation techniques, LKM-Self and LKM-Other, were independently compared with non-meditation. For each mental task, five sessions of data collected from each of the twelve participants were placed in a common data pool, from which randomly selected session data were used for training and testing the machine learning algorithms. Three previously tested BCI pipelines were used. In each case, feature extraction was performed using common spatial patterns (CSPs), short-time Fourier transform (STFT), or a fusion of CSP and STFT, followed by classification using a neural network structure. This study was further divided into three cases, where two, three, or four session pairs were used to train the algorithms, and the remaining session pair was used for testing. For each individual instance, the test was repeated thirty times to generalize the results. Thus, a total of 9900 independent tests were conducted for the entire dataset. The mean classification accuracies obtained in this study were lower than those reported in our previous intra-subject classification study. For example, in LKM-Self/non-meditation classification using three session pairs with the CSP + STFT pipeline, the mean accuracy for all tests was 62.3%, with the bottom 50% at 46.0% and the top 50% at 78.3%, demonstrating variability across session selections. The corresponding intra-subject classification result for the same instance was 72.1%. For all other instances, a similar pattern was observed. Furthermore, when considering all mean accuracies obtained, in 83.3% of the instances, CSP + STFT produced better classification accuracies than CSP or STFT alone. At the same time, in 75.0% of the instances, increasing the number of training session pairs led to improved classification accuracies. This study demonstrates that the subject-independent, multiple-session EEG classification of meditation and non-meditation is feasible for specific session combinations. Further research is needed to confirm these findings across larger and more diverse participant groups. These findings provide a foundation for developing subject-independent algorithms that can guide long-term meditation practice. Full article
(This article belongs to the Special Issue Sensing Technologies and Machine Learning for Cognitive and Physiological Monitoring)
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