Novel Approaches to EEG Signal Processing
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biosensors".
Deadline for manuscript submissions: closed (30 September 2020) | Viewed by 59454
Special Issue Editors
Interests: computational intelligence; biomedical signal processing; data fusion; evaluation; complexity analysis; deep learning; brain complex networks; non-destructive testing & entropic methods
Interests: deep learning; brain-apparatus communication; machine learning; biomedical engineering; medical imaging processing; computational neuroscience; brain network analysis
Interests: sparse signal recovery; machine learning; blind signal separation; wearable healthcare; biomedical signal processing; data mining for neurological disease study; brain-computer interfaces
Special Issue Information
Dear Colleagues,
Electroencephalography (EEG) is today a routinely well-established diagnostic technique that has extensively been applied to study brain functioning in health and disease. EEG signal processing involves the acquisition, analysis and treatment of the electrical activity of the brain measured using electroencephalography. The recent advances in signal processing and machine learning for EEG data processing have brought impressive progress in terms of solving several practical and challenging problems in many areas such as healthcare, biomedicine, biomedical engineering, BCI and biometrics.
The aim of this Special Issue is to present and discuss the most recent advances in EEG signal analysis and processing. We are inviting original research work covering novel theories, innovative methods, advanced technologies, fusion with other diagnostics and meaningful applications that can potentially lead to significant advances in EEG data analytics.
Prof. Dr. Francesco Carlo Morabito
Dr. Yangsong Zhang
Dr. Zhilin Zhang
Guest Editors
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Keywords
- EEG Signal Processing
- Brain–Computer Interface
- Machine Learning
- Deep Learning
- High-Density EEG
- Convolutional Neural Networks
- EEG Autoencoders
- Generative Adversarial Networks for EEG
- Data Fusion Techniques for Brain Data
- EEG Data Analytics
- EEG Compressive Sensing
- Brain States Classification from EEG
- Advanced artefact reduction in EEG
- EEG Feature Selection and Generation
- Wearable EEG
- Virtual Reality with EEG
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