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Advanced Sensors and Signal Processing for Psychophysiological Monitoring

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

Deadline for manuscript submissions: closed (15 May 2026) | Viewed by 34732

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Dr. Yvonne Tran

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School of Architecture, Design and Planning, The University of Sydney, Camperdown, Australia
Interests: biomedical signals; psychophysiology; injury; electroencephalography; heart rate variability; machine learning for rehabilitation medicine
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, advancements in wearable sensors, contactless monitoring technologies, and signal processing have enhanced our capability to track psychophysiological states more accurately in real-world settings. From heart rate variability and skin conductance to neural and ocular biomarkers, the integration of sophisticated sensing systems has unveiled new horizons in understanding cognitive load, stress, fatigue, and emotional regulation. These advancements present significant potential for applications in healthcare, human performance, and adaptability technologies.

This Special Issue explores the latest advancements in sensor technologies and signal processing methodologies that enhance psychophysiological monitoring.

Key areas of focus include the following:

Innovations in wearable and contactless sensors, including miniaturised and flexible bioelectronic systems, multimodal wearables, and passive sensing technologies.
Machine learning methods for feature extraction, such as deep learning models for interpreting physiological signals, automated noise reduction techniques, and personalised calibration strategies.
Multimodal data integration to improve reliability and interpretation, including sensor fusion strategies combining physiological, behavioural, and contextual data.
Advances in real-time processing and edge computing, enabling fast, low-latency analysis for continuous monitoring applications.
Novel signal processing techniques, such as adaptive filtering, wavelet transforms, and graph-based methods for detecting subtle physiological changes.
Validation studies in controlled and naturalistic settings, addressing challenges in terms of the accuracy, usability, and generalisability of psychophysiological sensors.
Applications in mental health, cognitive workload assessment, human–computer interaction, and clinical interventions, showcasing how these technologies are reshaping research and practice.

This Special Issue, bridging engineering, neuroscience, and applied physiology, highlights emerging solutions that extend the boundaries of real-time and continuous monitoring. Wearable sensors in particular continue to enhance psychophysiological monitoring by offering continuous, unobtrusive, and high-resolution data streams that deepen our understanding of human physiology in dynamic settings.

We welcome contributions that advance the field via novel sensor designs, enhanced signal processing algorithms, and practical applications across various domains. By bringing together interdisciplinary perspectives, this Special Issue aims to define the state of the art and pave the way for future innovations in psychophysiological sensing.

Dr. Yvonne Tran
Guest Editor

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Keywords

innovations in wearable and contactless sensors, including miniaturised and flexible bioelectronic systems, multimodal wearables, and passive sensing technologies
machine learning methods for feature extraction, such as deep learning models for interpreting physiological signals, automated noise reduction techniques, and personalised calibration strategies
multimodal data integration to improve reliability and interpretation, including sensor fusion strategies combining physiological, behavioural, and contextual data
advances in real-time processing and edge computing, enabling fast, low-latency analysis for continuous monitoring applications
novel signal processing techniques, such as adaptive filtering, wavelet transforms, and graph-based methods for detecting subtle physiological changes
validation studies in controlled and naturalistic settings, addressing challenges in terms of the accuracy, usability, and generalisability of psychophysiological sensors
applications in mental health, cognitive workload assessment, human–computer interaction, and clinical interventions, showcasing how these technologies are reshaping research and practice

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Published Papers (8 papers)

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Research

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16 pages, 995 KB

Open AccessArticle

EEG and IMU Gait Signal Processing: A Comparative Assessment of the “Reza” Exponential Filter and Classical Filters

by Reza Pousti, Daniel M. Russell, Derek C. Monroe and Christopher K. Rhea

Sensors 2026, 26(5), 1719; https://doi.org/10.3390/s26051719 - 9 Mar 2026

Viewed by 745

Abstract

Noise degrades both EEG and gait signals, and classical IIR filters (Butterworth, Chebyshev, elliptic) involve trade-offs between passband flatness, ripple, and roll-off. This study compared a novel exponential “Reza” filter with these designs for neural and locomotor data. We analyzed an open-source mobile [...] Read more.

256

-channel,

512 H z

; IMUs: six APDM Opals,

128 H z

). EEG channels were grand-averaged and band-pass filtered at

0.5 - 50 H z

, while IMU axes were averaged and band-pass filtered at

0.5 - 5 H z

. The outcomes were signal-to-noise ratio SNR (dB) and band-integrated Welch PSD (

E E G : 0.5 - 50 H z; I M U : 0.5 - 5 H z

). Repeated-measures ANOVAs tested the effect of filter types (Butterworth, Chebyshev I, elliptic, Reza) with Bonferroni-adjusted post hoc tests for the six pairwise filter comparisons (

α_{a d j} = 0.0083

). We reported partial eta-squared (

η p^{2}

) as the ANOVA effect size. For EEG, PSD did not differ among filters

(p = 0.146)

, whereas SNR differed strongly

(p < 0.001)

: Chebyshev and elliptic yielded the highest mean SNR and did not differ from each other, while both exceeded Butterworth, Reza was the lowest. For IMU, both SNR

(p < 0.001

) and PSD

(p < 0.001)

differed: Reza produced the highest mean SNR (significantly exceeding elliptic and Chebyshev), while Butterworth exceeded Chebyshev; meanwhile, IMU PSD showed a clear ordering with Reza retaining the most motion-band power, followed by Butterworth, then Chebyshev, with elliptic retaining the least. These results showed that filter choice materially shapes EEG and gait outcomes. For EEG, Chebyshev maximized SNR, while elliptic and Reza maintained comparable fidelity. For IMU gait signals, Reza matched Butterworth for denoising and preserved more signal power. Therefore, filter choice should be guided by the target outcome (SNR vs. band power) rather than a single default design. Full article

(This article belongs to the Special Issue Advanced Sensors and Signal Processing for Psychophysiological Monitoring)

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34 pages, 4605 KB

Open AccessArticle

Forehead and In-Ear EEG Acquisition and Processing: Biomarker Analysis and Memory-Efficient Deep Learning Algorithm for Sleep Staging with Optimized Feature Dimensionality

by Roberto De Fazio, Şule Esma Yalçınkaya, Ilaria Cascella, Carolina Del-Valle-Soto, Massimo De Vittorio and Paolo Visconti

Sensors 2025, 25(19), 6021; https://doi.org/10.3390/s25196021 - 1 Oct 2025

Viewed by 3647

Abstract

Advancements in electroencephalography (EEG) technology and feature extraction methods have paved the way for wearable, non-invasive systems that enable continuous sleep monitoring outside clinical environments. This study presents the development and evaluation of an EEG-based acquisition system for sleep staging, which can be adapted for wearable applications. The system utilizes a custom experimental setup with the ADS1299EEG-FE-PDK evaluation board to acquire EEG signals from the forehead and in-ear regions under various conditions, including visual and auditory stimuli. Afterward, the acquired signals were processed to extract a wide range of features in time, frequency, and non-linear domains, selected based on their physiological relevance to sleep stages and disorders. The feature set was reduced using the Minimum Redundancy Maximum Relevance (mRMR) algorithm and Principal Component Analysis (PCA), resulting in a compact and informative subset of principal components. Experiments were conducted on the Bitbrain Open Access Sleep (BOAS) dataset to validate the selected features and assess their robustness across subjects. The feature set extracted from a single EEG frontal derivation (F4-F3) was then used to train and test a two-step deep learning model that combines Long Short-Term Memory (LSTM) and dense layers for 5-class sleep stage classification, utilizing attention and augmentation mechanisms to mitigate the natural imbalance of the feature set. The results—overall accuracies of 93.5% and 94.7% using the reduced feature sets (94% and 98% cumulative explained variance, respectively) and 97.9% using the complete feature set—demonstrate the feasibility of obtaining a reliable classification using a single EEG derivation, mainly for unobtrusive, home-based sleep monitoring systems. Full article

(This article belongs to the Special Issue Advanced Sensors and Signal Processing for Psychophysiological Monitoring)

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15 pages, 2317 KB

Open AccessArticle

An Ensemble-Based AI Approach for Continuous Blood Pressure Estimation in Health Monitoring Applications

by Rafita Haque, Chunlei Wang and Nezih Pala

Sensors 2025, 25(15), 4574; https://doi.org/10.3390/s25154574 - 24 Jul 2025

Cited by 1 | Viewed by 3184

Abstract

Continuous blood pressure (BP) monitoring provides valuable insight into the body’s dynamic cardiovascular regulation across various physiological states such as physical activity, emotional stress, postural changes, and sleep. Continuous BP monitoring captures different variations in systolic and diastolic pressures, reflecting autonomic nervous system activity, vascular compliance, and circadian rhythms. This enables early identification of abnormal BP trends and allows for timely diagnosis and interventions to reduce the risk of cardiovascular diseases (CVDs) such as hypertension, stroke, heart failure, and chronic kidney disease as well as chronic stress or anxiety disorders. To facilitate continuous BP monitoring, we propose an AI-powered estimation framework. The proposed framework first uses an expert-driven feature engineering approach that systematically extracts physiological features from photoplethysmogram (PPG)-based arterial pulse waveforms (APWs). Extracted features include pulse rate, ascending/descending times, pulse width, slopes, intensity variations, and waveform areas. These features are fused with demographic data (age, gender, height, weight, BMI) to enhance model robustness and accuracy across diverse populations. The framework utilizes a Tab-Transformer to learn rich feature embeddings, which are then processed through an ensemble machine learning framework consisting of CatBoost, XGBoost, and LightGBM. Evaluated on a dataset of 1000 subjects, the model achieves Mean Absolute Errors (MAE) of 3.87 mmHg (SBP) and 2.50 mmHg (DBP), meeting British Hypertension Society (BHS) Grade A and Association for the Advancement of Medical Instrumentation (AAMI) standards. The proposed architecture advances non-invasive, AI-driven solutions for dynamic cardiovascular health monitoring. Full article

(This article belongs to the Special Issue Advanced Sensors and Signal Processing for Psychophysiological Monitoring)

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Review

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23 pages, 1007 KB

Open AccessReview

Interpolation and Imputation Strategies for Missing Segments in Continuous Pressure-Flow Cerebral Bio-Signals: A Systematic Scoping Review

by Isuru Sachitha Herath, Izabella Marquez, Julia Ryznar, Xue Nemoga-Stout, Yushu Shao, Rakibul Hasan, Amanjyot Singh Sainbhi, Kevin Y. Stein, Nuray Vakitbilir, Noah Silvaggio, Mansoor Hayat, Jaewoong Moon, Tobias Bergmann and Frederick A. Zeiler

Sensors 2026, 26(10), 3134; https://doi.org/10.3390/s26103134 - 15 May 2026

Abstract

Objective: Continuous pressure-flow cerebral bio-signals are critical for monitoring cerebrovascular dynamics but are often disrupted by missing data segments caused by artifacts from a variety of sources. These missing segments refer to segments of the signal that do not contain any valid physiological data. Such interruptions fragment the signals, resulting in discontinuities that compromise their overall integrity. Therefore, reconstructing missing values and preserving signal continuity are essential for ensuring the stable computation of signal trajectories and the accuracy of derived cerebrovascular indices. Methods: To address this issue, this systematic scoping review aimed to identify and synthesize existing interpolation and imputation strategies for handling missing segments in continuous pressure-flow cerebral bio-signals. Following the Cochrane and Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, a comprehensive search of five electronic databases was conducted from their inception to 24 September 2024, and updated on 16 June 2025, using a detailed search string. Results: The initial searches yielded 19,403 results, and 8 studies were filtered and included in the review. All included studies employed interpolation techniques, such as linear and spline interpolation algorithms, to correct distorted signal segments. However, none of the included studies directly utilized interpolation or imputation strategies to reconstruct or completely fill missing data segments. Conclusions: This reveals a critical knowledge gap, as no study has systematically addressed the utilization of interpolation or imputation strategies for missing segments in pressure-flow cerebral bio-signals. Therefore, this systematic review emphasizes the need for specialized methodologies and standardized frameworks to enable reliable recovery of missing data segments in pressure-flow cerebral bio-signals, which is critical for advancing real-time neurocritical care monitoring and experimental neuroscience/psychological research. Significance: This systematic review lays the groundwork for future research into physiologically informed interpolation and imputation strategies for pressure-flow cerebral bio-signals in clinical and research applications. Full article

(This article belongs to the Special Issue Advanced Sensors and Signal Processing for Psychophysiological Monitoring)

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45 pages, 1990 KB

Open AccessReview

An Overview of Stress Analysis Based on Physiological Signals: Systematic Review of Open Datasets and Current Trends

by Chariklia Chatzaki and Manolis Tsiknakis

Sensors 2025, 25(23), 7108; https://doi.org/10.3390/s25237108 - 21 Nov 2025

Cited by 4 | Viewed by 6935

Abstract

This review uniquely integrates open access dataset taxonomy with methodological trends in stress analysis, outlining the experimental framework and highlighting key gaps in reproducibility and FAIR compliance. In this context, stress induction methods, ground truth labeling approaches, open access datasets, computational advances, and current challenges and limitations are reported. A systematic review over the last decade (2014–2024) identified thirty-two open access affective datasets eligible for stress-related research, encompassing multimodal physiological signals, including electroencephalography (EEG), electrocardiography (ECG), electrodermal activity (EDA), and respiration (Resp), as well as behavioral measures, such as motion, audiovisual, and eye tracking data. Recent developments in signal analysis methods (2023–2025) highlight the predominance of multimodal fusion, advances in deep and self-supervised learning, personalized/adaptive models, and the growing adoption of explainable Artificial Intelligence, while machine learning approaches continue to hold a fundamental role. Despite these advances, several limitations and challenges remain, including heterogeneous experimental designs, sensor variability, limited demographic representation, data synchronization and labeling, and class imbalance. An effective experimental framework for stress research should integrate individual demographics and traits, reliable stressors, and high-quality physiological recordings within a well-defined and bias-controlled protocol, thereby producing reliable data to support and validate computational stress modeling. Continued progress in sensing, experimental standardization, and interpretable modeling is essential to produce reproducible, interpretable, and generalizable models of stress and emotions. Full article

(This article belongs to the Special Issue Advanced Sensors and Signal Processing for Psychophysiological Monitoring)

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34 pages, 4041 KB

Open AccessEditor’s ChoiceReview

Sensor Technologies for Non-Invasive Blood Glucose Monitoring

by Jiale Shi, Raúl Fernández-García and Ignacio Gil

Sensors 2025, 25(12), 3591; https://doi.org/10.3390/s25123591 - 7 Jun 2025

Cited by 9 | Viewed by 16520

Abstract

Diabetes poses a significant global health challenge, underscoring the urgent need for accurate and continuous glucose monitoring technologies. This review provides a comprehensive analysis of both invasive and non-invasive sensor technologies, with a particular focus on antenna-sensors and their working principle. Key aspects, including the selection of substrates and conductive materials, fabrication techniques, and recent advancements in rigid and flexible antenna-sensor designs, are critically evaluated. Notably, textile antenna-sensors are gaining increasing attention due to their potential for seamless integration into daily clothing. Furthermore, the influence of the human body on antenna-sensor performance is examined, emphasizing the importance of human phantom simulation and fabrication for precise modeling and validation. Finally, this review highlights the current technical challenges in the development of flexible antenna-sensors and discusses their transformative potential in enabling next-generation, non-invasive, and patient-centric glucose monitoring solutions. Full article

(This article belongs to the Special Issue Advanced Sensors and Signal Processing for Psychophysiological Monitoring)

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Other

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27 pages, 1143 KB

Open AccessSystematic Review

Missing Data Gap Imputation Methods in Electroencephalogram (EEG) Signals: A Systematic Scoping Review

by Tobias Bergmann, Michael Movshovich, Yushu Shao, Julia Ryznar, Xue Nemoga-Stout, Izabella Marquez, Isuru Herath, Amanjyot Singh Sainbhi, Nuray Vakitbilir, Noah Silvaggio, Rakibul Hasan, Kevin Y. Stein, Hina Shaheen, Jaewoong Moon and Frederick A. Zeiler

Sensors 2026, 26(8), 2431; https://doi.org/10.3390/s26082431 - 15 Apr 2026

Viewed by 579

Abstract

Objective: Electroencephalogram (EEG) measures electrophysiological activity in the cerebral cortex and is broadly used across diagnostic, research, and clinical contexts. Missing data gaps are a pervasive issue in EEG signal recording, resulting from sensor failures and sensor disconnections, amongst other sources. To preserve a continuous signal describing underlying electrophysiological processes, imputation must be used to reconstruct these gaps. The aim of this review is to examine the methods that have been developed for missing data gap imputation in EEG signals. Methods: A search of five databases was conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. The search question examined existing algorithms for imputation in EEG signals. Results: The initial search yielded 17,490 results (an update included 1913 additional results). This review includes 16 articles presenting EEG gap imputation methods. These imputation methods were characterized as (i) tensor-based, (ii) machine learning and deep learning, and (iii) model-based and classical. Conclusions: Several of these methods achieved strong effectiveness for accurately reconstructing gaps in ‘ground truth’ EEG signals; however, the limited generalizability of many of the studies due to small datasets lacking adequate participant diversity as well as methodological differences made it impossible to describe a single leading method. Further, the reliance on full recordings for segment imputation in some methods could prove prohibitive to real-time imputation. Future study is required to rectify these limitations and to properly investigate computational latency and requirements. Significance: This work provides novel insights into existing methods for EEG gap imputation, as it identifies current shortcomings in the literature and paves a way for a more generalizable solution to be achieved through future work. Full article

(This article belongs to the Special Issue Advanced Sensors and Signal Processing for Psychophysiological Monitoring)

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12 pages, 851 KB

Open AccessSystematic Review

Plantar Pressure Distribution in Charcot–Marie–Tooth Disease: A Systematic Review

by Alberto Arceri, Antonio Mazzotti, Federico Sgubbi, Simone Ottavio Zielli, Laura Langone, GianMarco Di Paola, Lorenzo Brognara and Cesare Faldini

Sensors 2025, 25(14), 4312; https://doi.org/10.3390/s25144312 - 10 Jul 2025

Cited by 2 | Viewed by 2031

Abstract

Background: Charcot-Marie-Tooth (CMT) disease is a hereditary motor and sensory neuropathy that affects foot morphology and gait patterns, potentially leading to abnormal plantar pressure distribution. This systematic review synthesizes the existing literature examining plantar pressure characteristics in CMT patients. Methods: A comprehensive search was conducted across PubMed, Scopus, and Web of Science databases. Risk of bias was assessed using the Newcastle–Ottawa Scale. Results: Six studies comprising 146 patients were included. Four studies employed dynamic baropodometry, and two used in-shoe pressure sensors to evaluate the main plantar pressure parameters. The findings were consistent across different populations and devices, with a characteristic plantar-pressure profile of marked midfoot off-loading with peripheral overload at the forefoot and rearfoot, often accompanied by a lateralized center-of-pressure path and a prolonged pressure–time exposure. These alterations reflect both structural deformities and impaired neuromuscular control. Interventional studies demonstrated a load redistribution of pressure after corrective surgery, though residual lateral overload often persists. Conclusions: Plantar pressure mapping seems to be a valuable tool to identify high-pressure zones of the foot in order to personalize orthotic treatment planning, to objectively monitor disease progression, and to evaluate therapeutic efficacy. Further longitudinal studies with standardized protocols are needed to confirm these results. Full article

(This article belongs to the Special Issue Advanced Sensors and Signal Processing for Psychophysiological Monitoring)

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