Dear Colleagues,
Data analysis is vital for mining useful information behind the original imaging data of brain and living organs. Recent advances in signal processing technology and machine learning methods improve our understanding about how the brain works and how it interacts with other entities, with exciting potential to lead to innovative clinical and non-clinical applications. Most recently, deep learning (DL) has received increasing attention and achieved impressive progress for data analysis in biomedical engineering field.
The aim of this Special Issue is launched to present and discuss the most recent advances of DL methods emerging in the cognitive neuroscience, clinical neurology, neural engineering, etc. We are inviting original research work that covering novel theories, innovative methods, advanced technologies, instructive applications in experimental and clinical research, which can potentially lead to significant advances in the rapidly emerging interdisciplinary field of Brain-Apparatus Conversation (BAC).
The research fields including but not limited to:
- DL assisted physiological signal acquisition and denoising methods
- Brain source imaging technologies
- Brain mechanisms exploration invarious cognitive tasks
- Brain disease prediction and diagnosis methods
- Data analysis and data generation in various brain-non-living machine/apparatus interaction (BMI) and brain-living organ communication (BOC)
- DL assisted practical and clinical applications
- Brain-inspired DL models
- Brain-inspired interpretability analysis for DL
- Association between large-scale pretrained models and the brain mechanisms
- Research on the learning theory of the brain or DL
- Qualitative and quantitative reviews that focus on the development and application of DL methods
