Advances in Biomedical Signal Processing and Analysis

A special issue of Signals (ISSN 2624-6120).

Deadline for manuscript submissions: 31 January 2026 | Viewed by 500

Special Issue Editors


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Guest Editor
Department of Biomedical Engineering, Linköping University, Linköping, Sweden
Interests: biomedical signal processing; machine learning; statistical pattern recognition

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Guest Editor
1. Department of Information Science and Media Studies, University of Bergen, Bergen, Norway
2. Department of Biomedical Engineering, Linköping University, Linköping, Sweden
Interests: medical informatics; data mining; classification; artificial intelligence; machine learning; pattern recognition; clustering

Special Issue Information

Dear Colleagues,

Biomedical signals are key indicators of physiological activity within the human body, providing vital information regarding the health and well-being of the individual. The presence of various sources of distortion unavoidably contaminates the signals, making it challenging to extract diagnostic information from the signal due to the nonstationary and non-ergodic behaviors of physiological signals. The integration of stochastic and ensemble processing of biomedical signals has led to significant advancements in signal understanding, complemented by sophisticated machine learning methodologies for classification and representation. The idea of cyclic stochastic processing of biomedical signals has significantly enriched the processing task by leveraging statistical processing methods. However, it should be noted that this topic is applicable only to the physiological signals in which the cyclic characteristics are intrinsically introduced. The heart sound signal (or alternatively phonocardiogram), lung sound signal, electrocardiogram, and sleep electroencephalogram are well-known signals with cyclic characteristics. The Special Issue welcomes papers addressing the advanced methods for processing biomedical signals. The scope of this issue is sufficiently broad, encompassing various study objectives, including denoising, representation, optimization, and classification of biomedical signals. The integration of advanced deep learning methods with processing techniques for cyclic biomedical signals, such as heart sound signals, is particularly encouraged.

Dr. Arash Gharehbaghi
Prof. Dr. Ankica Babic
Guest Editors

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Keywords

  • cyclic biomedical signals
  • heart sound
  • lung sound
  • electroencephalogram (EEG)
  • electrocardiogram (ECG)

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Published Papers (1 paper)

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Research

18 pages, 1949 KB  
Article
EEG-Based Analysis of Motor Imagery and Multi-Speed Passive Pedaling: Implications for Brain–Computer Interfaces
by Cristian Felipe Blanco-Diaz, Aura Ximena Gonzalez-Cely, Denis Delisle-Rodriguez and Teodiano Freire Bastos-Filho
Signals 2025, 6(4), 52; https://doi.org/10.3390/signals6040052 - 1 Oct 2025
Viewed by 254
Abstract
Decoding motor imagery (MI) of lower-limb movements from electroencephalography (EEG) signals remains a challenge due to the involvement of deep cortical regions, limiting the applicability of Brain–Computer Interfaces (BCIs). This study proposes a novel protocol that combines passive pedaling (PP) as sensory priming [...] Read more.
Decoding motor imagery (MI) of lower-limb movements from electroencephalography (EEG) signals remains a challenge due to the involvement of deep cortical regions, limiting the applicability of Brain–Computer Interfaces (BCIs). This study proposes a novel protocol that combines passive pedaling (PP) as sensory priming with MI at different speeds (30, 45, and 60 rpm) to improve EEG-based classification. Ten healthy participants performed PP followed by MI tasks while EEG data were recorded. An increase in spectral relative power around Cz associated with both PP and MI was observed, varying with speed and suggesting that PP may enhance cortical engagement during MI. Furthermore, our classification strategy, based on Convolutional Neural Networks (CNNs), achieved an accuracy of 0.87–0.89 across four classes (three speeds and rest). This performance was also compared with the standard Common Spatial Patterns (CSP) and Linear Discriminant Analysis (LDA), which achieved an accuracy of 0.67–0.76. These results demonstrate the feasibility of multiclass decoding of imagined pedaling velocities and lay the groundwork for speed-adaptive BCIs, supporting future personalized and user-centered neurorehabilitation interventions. Full article
(This article belongs to the Special Issue Advances in Biomedical Signal Processing and Analysis)
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