Electrophysiological Signal Processing in Neurological Diseases

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 743

Special Issue Editor


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Guest Editor
School of Medicine, The Chinese University of Hong Kong, Shenzhen, China
Interests: innovative sensing; wearable system development and clinical translation of electrophysiological signals (EEG and EMG); mathematical algorithms and deep learning models for electrophysiological signal processing in early diagnosis of neurological diseases

Special Issue Information

Dear Colleagues,

Neurological diseases, such as epilepsy, Alzheimer’s disease, Parkinson’s disease, and stroke, present significant challenges to global healthcare. Electrophysiological signals—including electroencephalography (EEG), electromyography (EMG), magnetoencephalography (MEG), and local field potentials (LFP)—serve as critical windows into neural function and dysfunction. Recent advances in signal processing, coupled with the rapid development of artificial intelligence and wearable technology, have opened new avenues for the early detection, precise diagnosis, and continuous monitoring of these disorders.

This Special Issue aims to gather the latest research on advanced signal processing techniques applied to electrophysiological data in the context of neurological health. We invite researchers to submit original research papers, reviews, and short communications that address challenges in signal acquisition, artifact removal, feature extraction, and classification. Contributions focusing on multimodal fusion, real-time monitoring systems, and explainable AI for clinical decision support are particularly encouraged.

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

  • Advanced signal processing algorithms for EEG, MEG, EMG, and EOG;
  • Machine learning and deep learning applications in neurological diagnosis;
  • Wearable sensors and systems for long-term neurological monitoring;
  • Brain–computer interfaces (BCIs) for rehabilitation and assistance;
  • Multimodal data fusion (e.g., EEG-fMRI, EEG-Speech) for disease profiling;
  • Biomarker discovery for neurodegenerative diseases (Alzheimer’s, Parkinson’s);
  • Seizure detection, prediction, and localization;
  • Connectivity analysis and brain network modeling;
  • Real-time processing and low-power embedded systems for neuro-applications.

Prof. Dr. Shixiong Chen
Guest Editor

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Keywords

  • neurologic diseases
  • signal processing
  • EEG
  • EMG
  • neuroimaging
  • wearable devices

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Published Papers (1 paper)

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Research

17 pages, 2238 KB  
Article
The Cortical Contributions to Turning Performance Through Muscle Synergies in Parkinson’s Disease: A Mediation Study
by Mirabel Ewura Esi Acquah, Zengguang Wang, Wei Chen and Dongyun Gu
Bioengineering 2026, 13(4), 453; https://doi.org/10.3390/bioengineering13040453 - 13 Apr 2026
Viewed by 287
Abstract
Turning impairment is a major contributor to falls in Parkinson’s disease (PD), yet the mechanisms linking cortical dysfunction to altered motor behavior remain unclear. In particular, it is unknown whether disrupted cortical communication impairs turning by altering muscle coordination. This study investigates a [...] Read more.
Turning impairment is a major contributor to falls in Parkinson’s disease (PD), yet the mechanisms linking cortical dysfunction to altered motor behavior remain unclear. In particular, it is unknown whether disrupted cortical communication impairs turning by altering muscle coordination. This study investigates a novel mechanistic pathway: whether muscle synergy complexity mediates the relationship between cortical network connectivity and turning performance in PD. Specifically, electroencephalography (EEG) and electromyography (EMG) were recorded from 12 individuals with PD and 12 age-matched healthy controls during a 180° turning task. Directed cortical connectivity, muscle synergy complexity, and spatiotemporal turning performance were quantified. Mediation analysis was used to determine whether cortical influences on behavior operate indirectly through neuromuscular coordination. Compared to controls, individuals with PD performed slower turns with shorter stride lengths and reduced synergy complexity (p < 0.05), alongside altered frontal cortical connectivity (p < 0.05). Across participants, higher synergy complexity was associated with faster, longer strides (p < 0.04). Cortical connectivity strength strongly predicted synergy complexity (R2 = 0.66, p < 0.001) and exerted a significant indirect effect on turning performance (β = 0.312; 95% CI [0.072, 0.605]; p = 0.008). In PD, reliance on this indirect pathway increased with disease severity and poorer turning ability (r > 0.57, p < 0.03). This work establishes how muscle synergy complexity significantly mediates the relationship between cortical connectivity and turning performance in PD. Our findings provide evidence of a cortical–neuromuscular–behavioral pathway underlying turning deficits, highlighting coordination as a key target for neurorehabilitation. Full article
(This article belongs to the Special Issue Electrophysiological Signal Processing in Neurological Diseases)
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