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Advanced Sensor Technologies for Neuroimaging and Neurorehabilitation
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Advanced Sensor Technologies for Neuroimaging and Neurorehabilitation

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

Deadline for manuscript submissions: 30 October 2026 | Viewed by 1524

Special Issue Editor

Dr. Fares Al-Shargie

E-Mail Website
Guest Editor
Department of Rehabilitation and Movement Sciences, Rutgers University, Newark, NJ, USA
Interests: neuroimaging; rehabilitation; artificial intelligence; brain–computer interface; neuromodulation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advances in sensor technologies, materials, and embedded systems are transforming neuroimaging and neurorehabilitation. These innovations enable precise, multimodal, and often wearable monitoring of neural, motor, and physiological functions in both clinical and real-world environments. This, in turn, supports the development of quantitative biomarkers, adaptive interventions, and home-based monitoring solutions for individuals with neurological disorders.

This Special Issue aims to gather original research and comprehensive reviews on advanced sensor systems for experimental neuroimaging and neurorehabilitation applications. We particularly welcome studies that involve human participants or relevant preclinical models in conditions such as stroke, multiple sclerosis, spinal cord injury, traumatic brain injury, Parkinson’s disease, and other central nervous system disorders, where sensors are used to assess function, monitor recovery, or deliver therapeutic interventions.

Topics of interest include, but are not limited to, the following:

  • Integration of novel sensors with neuroimaging modalities (e.g., EEG, fNIRS, MRI, MEG, and PET) for monitoring brain activity during motor, cognitive, or dual‑task rehabilitation protocols.
  • Wearable, implantable, and textile-based sensors for continuous monitoring of electrophysiology, kinematics, kinetics, and autonomic function in neurorehabilitation programs for stroke, multiple sclerosis, spinal cord injury, and related conditions.
  • Sensor-driven, closed-loop systems (e.g., brain–computer interfaces, neuromodulation, rehabilitation robotics, exoskeletons, and functional electrical stimulation) that adapt assistance or stimulation based on real-time brain and movement signals.
  • Sensor-enabled virtual, augmented, and mixed reality environments; serious games; and telerehabilitation platforms providing quantitative feedback and remote supervision in neurological populations.
  • Multimodal sensor fusion and advanced signal processing, including connectivity analysis, graph theory, and digital biomarker extraction, to characterize neuroplasticity, prognosis, and treatment response.
  • Machine-learning- and AI-based analytics for sensor data, including classification, outcome prediction, patient stratification, and personalization of therapy parameters in neurorehabilitation.
  • Wireless, low‑power, and edge-computing sensor platforms for in‑home and community monitoring, as well as long-term follow‑up of brain and motor function.
  • Validation, reliability, and responsiveness studies of sensor-derived outcome measures against established clinical scales in neurological rehabilitation.

Only submissions that include original experimental data (e.g., human or animal studies, or bench testing directly linked to neuroimaging or neurorehabilitation use cases) fall within the scope of this Special Issue. Methodological, technical, and feasibility studies are welcome if they clearly demonstrate and evaluate sensor performance in relevant neurorehabilitation or neuroimaging scenarios.

Dr. Fares Al-Shargie
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

  • sensor technology
  • wearable sensors
  • neuroimaging
  • neurorehabilitation
  • brain–computer interfaces
  • real-time monitoring
  • virtual reality
  • machine learning

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Published Papers (3 papers)

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Research

16 pages, 472 KB  
Article
Accelerated Brain Aging Identifies Functional Vulnerability Beyond Chronological Age in Multiple Sclerosis
by Patrick G. Monaghan, Taylor N. Takla, James H. Cole and Nora E. Fritz
Sensors 2026, 26(8), 2442; https://doi.org/10.3390/s26082442 - 16 Apr 2026
Viewed by 267
Abstract
Chronological age incompletely captures neurodegenerative burden and functional vulnerability in multiple sclerosis (MS). Brain-predicted age difference (Brain-PAD; predicted minus chronological age) provides an MRI-derived index of accelerated brain aging, but links to mobility and real-world behavior remain unclear. Forty-three adults with MS completed [...] Read more.
Chronological age incompletely captures neurodegenerative burden and functional vulnerability in multiple sclerosis (MS). Brain-predicted age difference (Brain-PAD; predicted minus chronological age) provides an MRI-derived index of accelerated brain aging, but links to mobility and real-world behavior remain unclear. Forty-three adults with MS completed structural MRI, mobility testing, and six months of free-living physical activity monitoring. Brain age was estimated using PyBrainAge applied to FreeSurfer-derived cortical thickness and subcortical volumes. Hierarchical regressions tested whether Brain-PAD explained additional variance in mobility (Mini-BESTest total and subscores; forward/backward walking velocity) and moderate-to-vigorous physical activity (MVPA) beyond age and disability (PDDS). Predicted brain age exceeded chronological age (Brain-PAD = 8.4 ± 11.1 years; p < 0.001). After accounting for age and PDDS, Brain-PAD explained additional variance in Mini-BESTest total (ΔR2 = 0.05, p = 0.042) and anticipatory control (ΔR2 = 0.08, p = 0.034), with a trend for sensory orientation. Brain-PAD was not associated with walking velocity beyond PDDS. Higher Brain-PAD was associated with lower MVPA (β = −0.91, p = 0.005) and explained additional variance (ΔR2 = 0.19). Brain-PAD is elevated in MS and relates to balance control and real-world physical activity beyond age and disability, highlighting its potential to identify functional vulnerability. Full article
(This article belongs to the Special Issue Advanced Sensor Technologies for Neuroimaging and Neurorehabilitation)
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15 pages, 1962 KB  
Article
Design and Performance Evaluation of a Low-Cost High-SNR EOG Sensing System for Arabic Locked-In Syndrome Communication
by Saleh I. Alzahrani, Najat Alomari, Sarah Alkilani, Lama Alghamdi and Bushra Melhem
Sensors 2026, 26(8), 2425; https://doi.org/10.3390/s26082425 - 15 Apr 2026
Viewed by 256
Abstract
Locked-in Syndrome (LIS) is a neurological condition in which individuals remain conscious but experience complete paralysis of voluntary muscles, except for eye movements—highlighting the need for reliable assistive communication technologies. This study presents the design and evaluation of an Arabic electrooculogram (EOG)-based communication [...] Read more.
Locked-in Syndrome (LIS) is a neurological condition in which individuals remain conscious but experience complete paralysis of voluntary muscles, except for eye movements—highlighting the need for reliable assistive communication technologies. This study presents the design and evaluation of an Arabic electrooculogram (EOG)-based communication system with adaptive classification capabilities for LIS applications. A custom-designed EOG acquisition circuit incorporating filtering and amplification stages was implemented and compared with the OpenBCI Cyton board. The system employed a hybrid classification approach combining amplitude, temporal, and statistical features to distinguish between blinks and voluntary vertical eye movements. Testing with ten healthy subjects yielded a mean classification accuracy of 83.96% ± 4.59% and an information transfer rate of 10.43 letters per minute, corresponding to a 30.38% improvement over conventional approaches. The custom-designed circuit achieved a signal-to-noise ratio of 25.21 dB, outperforming the OpenBCI Cyton board by 8% while reducing system cost by 62%. The integration with a Morse code-based interface enabled Arabic letter composition, while the system incorporated auto-completion and text-to-speech functionalities to further enhance communication efficiency. This cost-effective solution addresses a critical gap in assistive technologies for Arabic-speaking individuals with LIS and shows strong potential for enhancing their communication abilities and overall quality of life. Full article
(This article belongs to the Special Issue Advanced Sensor Technologies for Neuroimaging and Neurorehabilitation)
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14 pages, 4736 KB  
Article
Unsupervised Dynamic Time Warping Clustering for Robust Functional Network Identification in fNIRS Motor Tasks
by Murad Althobaiti
Sensors 2026, 26(6), 1848; https://doi.org/10.3390/s26061848 - 15 Mar 2026
Cited by 1 | Viewed by 452
Abstract
Functional near-infrared spectroscopy (fNIRS) is a valuable non-invasive modality for brain-computer interfaces (BCIs), but robust signal interpretation is challenged by the significant temporal variability of the hemodynamic response. Standard linear methods, such as Pearson correlation, often fail to capture functional connectivity when signals [...] Read more.
Functional near-infrared spectroscopy (fNIRS) is a valuable non-invasive modality for brain-computer interfaces (BCIs), but robust signal interpretation is challenged by the significant temporal variability of the hemodynamic response. Standard linear methods, such as Pearson correlation, often fail to capture functional connectivity when signals exhibit temporal jitter. This study validates an unsupervised Dynamic Time Warping (DTW) clustering framework to robustly identify motor networks from fNIRS data by accommodating non-linear temporal shifts. We analyzed a public fNIRS dataset (N = 30) across right-hand (RHT), left-hand (LHT), and foot tapping (FT) tasks. A robust preprocessing pipeline was implemented, including Wavelet Motion Correction and Common Average Referencing (CAR) to remove artifacts and global systemic noise. The core method involved computing Z-score normalized DTW distance matrices, followed by hierarchical clustering. To validate the framework, we benchmarked it against a standard Pearson Correlation method. Results show that the unsupervised DTW framework achieved a network identification accuracy of 53.17%, significantly outperforming the standard Pearson correlation benchmark (48.06%) with a statistically significant difference (p < 0.05). The framework successfully detected distinct, somatotopically correct modulations: superior-medial activation during foot tapping and lateralized activation during hand tapping. These findings demonstrate that unsupervised DTW clustering is a robust, data-driven approach that outperforms conventional linear methods in capturing functional networks during motor tasks, showing significant potential for next-generation asynchronous BCIs. Full article
(This article belongs to the Special Issue Advanced Sensor Technologies for Neuroimaging and Neurorehabilitation)
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