Biosignal Sensing Analysis (EEG, EMG, ECG, PPG) (2nd Edition)

Dear Colleagues,

Biosignals, generated by the electrical, physiological, and biochemical activities of the human body, provide invaluable insights into tissue and organ functions. This Special Issue delves into advanced methods for capturing and processing biosignals, emphasizing wearable technologies that measure signals such as ECG (electrocardiogram), PPG (photoplethysmogram), EEG (electroencephalogram), EMG (electromyogram), EDR (electrodermal response), and NIRS (near-infrared spectroscopy). These modalities enable the monitoring of cardiac, neural, muscular, and autonomic activities, as well as tissue oxygenation and hemodynamics.

In addition to exploring the tools and techniques used to record these signals, the focus of this Special Issue extends to other critical aspects of biosignal processing, including artifact rejection and biomarker extraction, which are essential for enhancing the quality of data and deriving meaningful insights. These advancements have broad applications, ranging from health monitoring and emotion recognition to diagnostics and personalized medicine. This Special Issue aims to provide a comprehensive overview of the functionality, applications, and transformative potential of these technologies, highlighting their role in advancing biomedical innovation and multi-modal health assessments.

The scope of this Special Issue includes the following topics:

1. Biomedical signals (ECG, EEG, PPG, EDR, EMG, etc.):
   Biomedical signals represent physiological activities measured from the body. Advances in wearable technologies have significantly improved signal acquisition, offering higher resolution and accuracy in compact, low-power devices. Modern systems integrate advanced signal processing, such as adaptive filtering and machine learning algorithms, to extract features such as heart rate variability (ECG), brain activity patterns (EEG), vascular compliance (PPG), sweat gland response (EDR), and muscle activity (EMG) with real-time feedback for diagnostics and monitoring.
2. Portable monitoring:
   Portable monitoring solutions now utilize multimodal sensors, Bluetooth-enabled devices, and cloud-based platforms to enable continuous health tracking. Wearable patches and portable biosignal recorders can collect high-quality data over extended periods, even in ambulatory settings. Applications include chronic disease management, early disease detection, and telemedicine, powered by AI algorithms for data analysis and predictive modeling.
3. Continuous sleep-apnea screening in an unattended home setting:
   Continuous sleep apnea screening devices leverage multimodal biosignal processing (ECG, respiratory effort, SpO2, and chest movements). FDA-cleared wearable devices, like the Huxley SANSA patch, use AI for detecting apnea events, offering at-home alternatives to traditional polysomnography. Advances in algorithms enable the automatic classification of apnea severity and improve compliance through unobtrusive, user-friendly designs.
4. Detection of nightly snore events in patients with OSA (Obstructive Sleep Apnea):
   Acoustic sensors, accelerometers, and contact-based microphones integrated into wearables are commonly used to detect snore events. AI-powered algorithms analyze sound frequency, amplitude, and temporal patterns to classify snores and identify apneas. Multimodal approaches also use respiratory and oxygen saturation data for more precise apnea detection.
5. Multimodal brain signal processing of EEG/MEG/fMRI/fNIRS:
   The integration of multiple modalities such as EEG, MEG (magnetoencephalography), fMRI, and fNIRS allows a comprehensive understanding of brain activity. Cutting-edge techniques focus on leveraging multimodal data fusion for cognitive neuroscience, brain–computer interfaces (BCIs), and neurorehabilitation. Machine learning enhances feature extraction and improves the accuracy of functional connectivity analyses.
6. Multimodal biosignal processing for body area networks:
   Body Area Networks (BANs) now integrate multimodal signals (ECG, PPG, SpO2, accelerometry) using low-power wireless communication protocols. Advances focus on secure, energy-efficient data transmission and the real-time fusion of multimodal data for health monitoring, with applications in sports, elderly care, and chronic disease management.
7. Hybrid BCI using multimodal signals:
   Hybrid BCIs combine EEG with signals like EMG, EOG (electrooculography), or fNIRS to improve the reliability and performance of brain–computer interfaces. These systems enhance signal discrimination, reduce false positives, and extend applications into motor rehabilitation, neuroprosthetics, and assistive technologies for individuals with disabilities.
8. Multimodal signal processing for wearable devices:
   Wearable devices now integrate multimodal sensors to simultaneously track physiological, behavioral, and environmental data. For instance, devices combining ECG, PPG, and accelerometry provide insights into cardiovascular health and activity patterns. AI-enabled fusion algorithms improve interpretation, enabling precision health interventions.
9. Emerging applications of multimodal signal processing technology:
   Emerging applications include emotion detection, mental health monitoring, personalized fitness tracking, and early disease prediction. Multimodal biosignal processing also finds use in smart homes, autonomous vehicles (driver monitoring), and wearable IoT systems, paving the way for more holistic health and wellness solutions.
10. Methodologies for multimodal fusion and integration:
    Recent advancements in multimodal fusion methodologies include the use of deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to process and integrate diverse data streams. Techniques such as feature-level fusion and decision-level fusion enhance accuracy in detecting complex patterns and enable the application robust multimodal systems in healthcare and beyond.

Dr. Vangelis Sakkalis
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.

• biomedical signals
• biosignals
• ECG
• EEG
• PPG
• EDR
• EMG
• BCI
• multimodal signal processing
• biomedical sensing

• Biosignal Sensing Analysis (EEG, MEG, ECG, PPG) in Sensors (11 articles)