AI/ML-Driven EEG Signal Processing
A special issue of Computers (ISSN 2073-431X). This special issue belongs to the section "AI-Driven Innovations".
Deadline for manuscript submissions: 31 October 2027 | Viewed by 160
Special Issue Editors
Interests: brain-computer interface; EEG
Special Issue Information
Dear Colleagues,
Electroencephalography (EEG) is a powerful tool for studying brain activity and has wide applications in clinical diagnostics, neuroscience research, and human–computer interaction. The integration of artificial intelligence (AI) and machine learning (ML) techniques into EEG signal processing has revolutionized this field, enabling more accurate analysis, real-time classification, and anomaly detection. With rapid advancements in AI/ML, researchers can now leverage these techniques to enhance the interpretation of complex EEG data, leading to improvements in the early diagnosis of neurological disorders, brain–computer interfaces (BCIs), and neurofeedback applications.
This Special Issue, titled "AI/ML-Driven EEG Signal Processing," invites contributions that explore the latest innovations in AI and ML for EEG analysis. We encourage papers that address both the theoretical foundations and practical applications of these technologies, including new models, algorithms, and real-world use cases. The goal of this Special Issue is to advance our understanding of EEG signal processing and to highlight the potential of AI and ML to transform clinical and research practices in neuroscience.
Contributions are welcome in areas including, but not limited to, the following:
- AI/ML-based EEG signal processing and analysis;
- Time-frequency analysis for EEG data;
- Feature extraction and selection techniques in EEG signals;
- Machine learning models for EEG classification and clustering;
- Deep learning applications for EEG big data;
- Brain–computer interfaces (BCIs) and neurofeedback;
- EEG-based biometrics and rehabilitation engineering.
Dr. Rui Zhang
Prof. Dr. Peiyang Li
Guest Editors
Manuscript Submission Information
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Keywords
- EEG signal processing
- machine learning for EEG
- deep learning
- brain–computer interfaces
- feature extraction
- time-frequency analysis
- EEG abnormalities detection
- neurofeedback
- big data in EEG
- cognitive neuroscience
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