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Advanced Sensing Techniques in Biomedical Signal Processing
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Advanced Sensing Techniques in Biomedical Signal Processing

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

Deadline for manuscript submissions: 30 December 2025 | Viewed by 114

Special Issue Editor

Prof. Dr. Dakun Lai

E-Mail Website
Guest Editor
School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: IOTs and wearable devices; biomedical imaging and signal processing; bioelectromagnetism and medical applications; AI-based diagnosis of cardiac/neuro-electrical disorders
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The diagnosis and prognosis of human heart and brain conditions in hospital predominantly rely on biomedical signals and sensor data processing; this medical data is analyzed to inform the study of a patient’s cardiac and neurological health. Currently, more innovative technologies and intelligent systems are required to process a larger volume of medical data and provide a higher quality of healthcare services, enabling the automatic and accurate detection of symptoms of diseases in their early stages.

The aim of this Special Issue is to present the latest innovative research from scholars and experts in the field of electrophysiological signal and image processing using computer pattern recognition and/or deep learning. This includes papers that cover areas such as biomedical engineering, computer vision, and Internet of Things, as well as theoretical and practical aspects of various sensors and information theory in medical image processing.

Original research and review articles for this Special Issue can address topics including, but not limited to, the following:

  • ECG/EEG sensing and signal processing;
  • Medical image processing for heart or brain imaging;
  • Bioinformatics for healthcare engineering;
  • Supervised/unsupervised learning algorithm for ECG/EEG diagnosis applications;
  • Explainable AI in ECG/EEG applications;
  • Image retrieval, segmentation, grouping and shape;
  • AI chips and their implantation of machine learning and deep learning algorithms;
  • Internet of Things (IOT);
  • Applying machine learning-empowered sensing to industrial scenarios

Prof. Dr. Dakun Lai
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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • deep learning
  • biomedical imaging
  • electrophysiological signal
  • heart
  • brain

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Published Papers (1 paper)

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Research

16 pages, 2489 KB  
Article
Sentence-Level Silent Speech Recognition Using a Wearable EMG/EEG Sensor System with AI-Driven Sensor Fusion and Language Model
by Nicholas Satterlee, Xiaowei Zuo, Kee Moon, Sung Q. Lee, Matthew Peterson and John S. Kang
Sensors 2025, 25(19), 6168; https://doi.org/10.3390/s25196168 - 5 Oct 2025
Abstract
Silent speech recognition (SSR) enables communication without vocalization by interpreting biosignals such as electromyography (EMG) and electroencephalography (EEG). Most existing SSR systems rely on high-density, non-wearable sensors and focus primarily on isolated word recognition, limiting their practical usability. This study presents a wearable [...] Read more.
Silent speech recognition (SSR) enables communication without vocalization by interpreting biosignals such as electromyography (EMG) and electroencephalography (EEG). Most existing SSR systems rely on high-density, non-wearable sensors and focus primarily on isolated word recognition, limiting their practical usability. This study presents a wearable SSR system capable of accurate sentence-level recognition using single-channel EMG and EEG sensors with real-time wireless transmission. A moving window-based few-shot learning model, implemented with a Siamese neural network, segments and classifies words from continuous biosignals without requiring pauses or manual segmentation between word signals. A novel sensor fusion model integrates both EMG and EEG modalities, enhancing classification accuracy. To further improve sentence-level recognition, a statistical language model (LM) is applied as post-processing to correct syntactic and lexical errors. The system was evaluated on a dataset of four military command sentences containing ten unique words, achieving 95.25% sentence-level recognition accuracy. These results demonstrate the feasibility of sentence-level SSR using wearable sensors through a window-based few-shot learning model, sensor fusion, and ML applied to limited simultaneous EMG and EEG signals. Full article
(This article belongs to the Special Issue Advanced Sensing Techniques in Biomedical Signal Processing)
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