EEG Signal Processing in Medical Diagnosis Applications
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".
Deadline for manuscript submissions: 10 August 2025 | Viewed by 122
Special Issue Editors
Interests: intelligent data analysis; data mining; computational intelligence (including broadly defined machine learning algorithms, neural networks and fuzzy logic); methods for detecting outliers in large datasets of various types (complex data, streams, images); applications to modelling, simulation, image analysis, pattern recognition, knowledge extraction, and the development of medical systems
Interests: computational intelligence (mainly neural networks, fuzzy systems, and genetic algorithms) and its applications to modelling, simulation, image analysis, pattern recognition, knowledge extraction, intelligent internet exploration, fault diagnosis of technical plants, and medical system development
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Special Issue Information
Dear Colleagues,
Electroencephalography (EEG) is a technique for recording the brain's electrical activity, widely used in medical diagnostics. EEG signals are measured using electrodes placed on the scalp, allowing the real-time assessment of brain function. Due to its non-invasiveness, relatively low cost, and diagnostic value, EEG is applied across various medical fields, including neurology, psychiatry, and anesthesiology.
This Special Issue focuses on the broad topic of EEG signal processing and analysis. It provides an opportunity to identify new research directions, present original research findings, and showcase practical applications. It also serves as a platform for sharing and disseminating innovative and practical experiences in the field.
The scope of this Special Issue includes, but is not limited to, the following research areas:
- Methods for EEG signal processing and analysis, particularly signal filtering techniques (e.g., artifact and noise removal), feature extraction algorithms, and EEG signal segmentation and classification;
- Implementing advanced algorithms based on machine learning and deep learning methods for EEG signal analysis and pattern detection;
- Comparative studies on the effectiveness of various EEG processing and analysis methods in medical diagnostics;
- Development of modern decision support systems (DSSs), including automated decision making and result interpretation in medical contexts;
- Innovative pattern detection methods for identifying exceptional features in EEG signals;
- Applications in personalized medicine and bioinformatics, such as integrating EEG with other biomarkers (e.g., fMRI, ECG) and analyzing EEG data from a Big Data perspective;
- EEG applications in the diagnosis of neurological diseases, including early detection of epilepsy (e.g., seizure analysis), recognition of neurodegenerative diseases (e.g., Alzheimer's, Parkinson's), sleep disorder studies (e.g., sleep stage analysis, sleep apnea), and the diagnosis of ADHD, depression, schizophrenia, and other psychiatric disorders;
- EEG applications in rehabilitation and therapy, such as neurofeedback and its therapeutic uses, supporting rehabilitation processes after strokes, and using EEG to evaluate the effectiveness of therapies;
- Design of devices for EEG data acquisition, including new sensors and wireless systems;
- Development of open-source platforms and tools for EEG signal analysis;
- Applications of real-time systems in EEG signal processing.
This Special Issue aims to foster innovative contributions and promote the practical implementation of EEG signal processing techniques in medical diagnostics.
Dr. Agnieszka Duraj
Prof. Dr. Piotr S. Szczepaniak
Guest Editors
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Keywords
- electroencephalography
- EEG data acquisition
- EEG signal analysis
- medical diagnosis
- neurological diseases
- signal filtering techniques
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